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1
MUSCILLO, ALESSIO. "Endogenous Diffusion in Social Networks. Two Cases: Infectious Diseases and Sharing of Knowledge." Doctoral thesis, Università di Siena, 2017. http://hdl.handle.net/11365/1059090.
Full textAbstract:
Complex phenomena arising from the interaction of ``elemental'' pieces have been first studied in physics and biology, where such constitutive particles were given deterministic rules for their behavior. In that context it was already clear that even critical outcomes can result on the aggregate level in situations where agents' behaviors are ``mechanic'' and ``simple''.
In recent years, inspired by real-world phenomena, economics and other social sciences have also started to play a role in this very wide strand of research. On the one hand, by introducing degrees of rationality in agents' behaviors and, on the other hand, by allowing heterogeneity in their interactions and responses to endogenous and exogenous stimuli.
This kind of reasoning has proven itself of particular success when applied in the context of social networks. Research on such intrinsically complex objects blossomed naturally within the realm of sociology, however it was only with the advent of the Internet, with the availability of large databases and the application of mathematical techniques from statistical physics that the field has really started its golden period of prosperity.
In this dissertation we contribute to this strand of literature by focusing on diffusive mechanisms that naturally emerge in the context of social networks. The first example is provided by the contagion of diseases channeled through social contacts, with possible straightforward applications to the cases of diffusion of opinions or of bad habits. The second example under study is that of knowledge diffusion (sharing?), which is not only typical of the academic world but also of innovation-seeking environments, such as that of research-and-development firms, where a collaboration network is constituted by the individuals.
A common feature of these cases is the fact that economic agents can endogenously and dynamically adapt by changing their (local) network of contacts or their response. In both examples, though, the impact of a single agent's action can reverberate through the whole system via its contacts (and its contacts' contacts, and so on). In the context of social networks, then, it becomes particularly challenging to understand how local features (behaviors or inclinations) may propagate, amplify or dissolute when embedded in the whole environment. One crucial difference with other approaches lies exactly in the fact that ``local'' neighborhoods can indeed be very different from one another and, moreover, very different from the global situation, which is the outcome at an aggregate level.
This dissertation is structured as follows. The first chapter describes a model of diffusion of a disease between two different locations, where the agents are able to respond and adapt to this menace. A peculiarity of our model is the possibility of agents of deciding where (i.e. with whom) to interact, in the attempt of avoiding contagion while still obtaining the benefits coming from the interactions with other healthy agents. The analytical results show that such individual-level behaviors have crucially different outcomes depending on the ``world'' these agents are living in: in particular, the two globally different systems considered (one, ``globalized'', where connections between the locations are allowed and the other, ``autarkic'', where they are forbidden) exhibit crucially different resistance to exogenous shocks in the infection rate. Further research in this field is still needed, as this model is one of the few attempts in the economics literature at trying to embed rational and responsive agents in a dynamical model of diffusion on networks. Applications to systemic risk and systemic resistance can benefit from this kind of research as well as analyses of mechanisms where is prevalent the interplay between local versus global forces.
The second chapter deals with a classic dilemma in the economics and business literature, that of exploration versus exploitation, and links it to the achievement of results, i.e. to the notion of performance. Specifically, we follow individual scientists throughout their careers and use their co-authorship and citation networks to map their ``knowledge space'', in order to measure their propensity to explore, both in terms of new topics and of new collaborations. Econometric results shows that the relationship between exploration and performance tends to exhibit an inverted-U shape, hence supporting the theory that a ``sweet'' spot where performance is maximized might exist, at least at an individual level.
Further research on this topic is still necessary, for example to understand in depth the relationship existing (if any) between forms of ``social exploration'' (i.e. exploration in terms of collaborations and social contacts) and ``scientific exploration'' (i.e. in terms of changes of the subjects studied or fields of expertise). Moreover, the results and techniques developed here can not only be directly applied to bibliometrics studies, but can also be fundamental to give the right incentives (and, possibly, funding) to encourage long-term innovation-seeking behaviors.
The third chapter tackles the same research question, but from a different viewpoint: what is the outcome of that analysis when the production units are ``aggregated'' at the level of (departments of) universities? At this aggregate level, it turns out that, in contrast to what seen in the previous chapter, a U-shaped curve characterizes the relationship between performance and exploration. Moreover, this relationship is also complicated by the effects of resources and size of each university. This complication can be seen as evidence of how, at this level, the interplay between economies of scale and economies of scope can generate an overall complex behavior. In this case too, then, the individual-level and the aggregate-level analysis exhibit once again very different outcomes: this underlines even more the complexity that comes out from the interactions in systems composed by different layers and levels.
2
Bimpikis, Kostas. "Strategic delay and information exchange in endogenous social networks." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62405.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 160-165).
This thesis studies optimal stopping problems for strategic agents in the context of two economic applications: experimentation in a competitive market and information exchange in social networks. The economic agents (firms in the first application, individuals in the second) take actions, whose payoffs depend on an unknown underlying state. Our framework is characterized by the following key feature: agents time their actions to take advantage of either the outcome of the actions of others (experimentation model) or information obtained over time by their peers (information exchange model). Equilibria in both environments are typically inefficient, since information is imperfect and, thus, there is a benefit in being a late mover, but delaying is costly. More specifically, in the first part of the thesis, we develop a model of experimentation and innovation in a competitive multi-firm environment. Each firm receives a private signal on the success probability of a research project and decides when and which project to implement. A successful innovation can be copied by other firms. We start the analysis by considering the symmetric environment, where the signal quality is the same for all firms. Symmetric equilibria (where actions do not depend on the identity of the firm) always involve delayed and staggered experimentation, whereas the optimal allocation never involves delays and may involve simultaneous rather than staggered experimentation. The social cost of insufficient experimentation can be arbitrarily large. Then, we study the role of simple instruments in improving over equilibrium outcomes. We show that appropriately-designed patents can implement the socially optimal allocation (in all equilibria) by encouraging rapid experimentation and efficient ex post transfer of knowledge across firms. In contrast to patents, subsidies to experimentation, research, or innovation cannot typically achieve this objective. We also discuss the case when signal quality is private information and differs across firms. We show that in this more general environment patents again encourage experimentation and reduce delays. In the second part, we study a model of information exchange among rational individuals through communication and investigate its implications for information aggregation in large societies. An underlying state (of the world) determines which action has higher payoff. Agents receive a private signal correlated with the underlying state. They then exchange information over their social network until taking an (irreversible) action. We define asymptotic learning as the fraction of agents taking an action that is close to optimal converging to one in probability as a society grows large. Under truthful communication, we show that asymptotic learning occurs if (and under some additional conditions, also only if) in the social network most agents are a short distance away from "information hubs", which receive and distribute a large amount of information. Asymptotic learning therefore requires information to be aggregated in the hands of a few agents. We also show that while truthful communication is not always optimal, when the communication network induces asymptotic learning (in a large society), truthful communication is an equilibrium. Then, we discuss the welfare implications of equilibrium behavior. In particular, we compare the aggregate welfare at equilibrium with that of the optimal allocation, which is defined as the strategy profile a social planner would choose, so as to maximize the expected aggregate welfare. We show that when asymptotic learning occurs all equilibria are efficient. A partial converse is also true: if asymptotic learning does not occur at the optimal allocation and an additional mild condition holds at an equilibrium, then the equilibrium is inefficient. Furthermore, we discuss how our learning results can be applied to several commonly studied random graph models, such as preferential attachment and Erdos-Renyi graphs. In the final part, we study strategic network formation in the context of information exchange. In particular, we relax the assumption that the social network over which agents communicate is fixed, and we let agents decide which agents to form a communication link with incurring an associated cost. We provide a systematic investigation of what types of cost structures and associated social cliques (consisting of groups of individuals linked to each other at zero cost, such as friendship networks) ensure the emergence of communication networks that lead to asymptotic learning. Our result shows that societies with too many and sufficiently large social cliques do not induce asymptotic learning, because each social clique would have sufficient information by itself, making communication with others relatively unattractive. Asymptotic learning results if social cliques are neither too numerous nor too large, in which case communication across cliques is encouraged.
by Kostas Bimpikis.
Ph.D.
3
Pedersen, Tavis Joseph. "Tracking infection diffusion in social networks." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/62557.
Full textAbstract:
This thesis explores the problem of tracking the diffusion of contagion processes on social networks. Infection (or Information) diffusion is modeled using the Susceptible-Infected-Susceptible (SIS) model. Mean field approximation is employed to approximate the discrete valued infection dynamics by a deterministic ordinary differential equation, thereby yielding a generative model for the infection diffusion. The infection is shown to follow polynomial dynamics and is estimated using an exact non-linear Bayesian filter. We compute posterior Cramer-Rao bounds to obtain the fundamental limits of the filter which depend on the structure of the network. The SIS model is extended to include homophily, and filtering on these networks using the exact non-linear Bayesian filter is illustrated. With consideration for the collaborative or antagonistic nature of some social processes on networks, we present an alternative, game theoretic, model for the spread of information based on the evolutionary Moran process. The diffusion of collaboration following this Moran model is estimated using a particle filter. Additionally, we validate the efficacy of our method with diffusion data from a real-world online social media platform, Twitter. We find that SIS model is a good fit for the information diffusion and the non-linear filter effectively tracks the information diffusion.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
4
Sun, Hongxian, and 孙鸿賢. "Modeling information diffusion in social networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48330127.
Full textAbstract:
Interpersonal communication with network infrastructure creates mobile and online social networks, which shorten the distance among people. It naturally leads to an important question asking for a clear and detailed description of information dissemination and diffusion process in social networks. An in-depth understanding of the question may help in various aspects,e.g., designing better communication protocols and predicting the demand of hot contents. In the thesis we focus on two concrete sub-questions.
The first one is to describe the performance of mobile social networks under the practical constraint that information is only allowed to be shared among mutual social friends. Existing designs for mobile social networks enable opportunistic message exchange whenever two mobile devices are within the transmission range of each other. However in real life, people may only be willing to interact with their social friends instead of anyone upon contact. Under such a constraint message forwarding may behave differently. We concentrate on modeling the end-end delivery delay in this scenario under two message validity models, unlimited validity and limited validity. In the first case nodes try their best to relay a message to the destination. While in the latter case a relay node will delete its local copy of a message after carrying it for some time T. Mean-field equations for the dynamic of the population of spreader nodes are derived. With solutions of these equations and empirical studies, we get insightful results. First, more skewed distribution of the number of friends leads to larger delay. Second, the unicast delay is almost constant rather than quickly decreasing with the network size. Last, with a moderate choice of T, we can guarantee almost 100% delivery with a delay very close to the case of unlimited validity. It signifies that a good trade-off can be obtained between delivery efficiency and energy/storage overhead.
The second sub-question asks for a model of information diffusion in online social networks. Sharing information in OSN platforms like Twitter and Facebook has become an important part of human life. Thus understanding the dynamic is important as it may help, for example, predict the demand of media contents. We make use of the age-dependent branching process framework to describe the diffusion of content with constant popularity. We give explicit expression for the expected diffusion cascade size, analyze its asymptotic behavior and compare it with the prediction of traditional, over-simplified epidemic model. Also we analyze the diffusion of content with time-variant popularity. An integral equation governing the growth of cascade size is given. Some measurement observations are also explained and quantified. Lastly we design a new model incorporating the geographical locality of contents based on the study of multitype age-dependent branching processes, where the expression for expected cascade size is also given, offering a clear picture of the whole process. Extensive simulations verify the analytical expressions and offer straightforward insights into some other properties of the diffusion process which are not captured by the mathematical formulation.published_or_final_version
Computer Science
Master
Master of Philosophy
5
DE, NICOLA ANTONIO. "Diffusion of interests in social networks." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2015. http://hdl.handle.net/2108/202331.
Full textAbstract:
This thesis provides a model for diusion of interests in Social Networks (SNs). It
demonstrates that the topology of the SN plays a crucial role in the dynamics of
the individual interests. Understanding cultural phenomena on SNs and exploiting
the implicit knowledge about their members is attracting the interest of dierent research
communities both from the academic and the business side. The community
of complexity science is devoting signicant eorts to dene laws, models, and theories,
which, based on acquired knowledge, are able to predict future observations
(e.g. success of a product). In the mean time, the semantic web community aims at
engineering a new generation of advanced services by dening constructs, models and
methods, adding a semantic layer to SNs. In this context, a leapfrog is expected to
come from a hybrid approach merging the disciplines above. Along this line, this work
focuses on the propagation of individual interests in social networks. The proposed
framework consists of the following main components: a method to gather information
about the members of the social networks; methods to perform some semantic
analysis of the Domain of Interest; a procedure to infer members' interests; and an
interests evolution theory to predict how the interests propagate in the network. As
a result, one achieves an analytic tool to measure individual features, such as members'
susceptibilities and authorities. Although the approach applies to any type of
social network, here it was tested against two research communities in the domain of
1
Abstract
computer science and physics. The DBLP (Digital Bibliography and Library Project)
database and the APS (American Physical Society) dataset were elected as test-cases
since they provide the most comprehensive list of scientic productions in their respective
elds.
6
Yang, Yile, and 楊頤樂. "Noncooperative information diffusion in online social networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206693.
Full textAbstract:
Information diffusion in online social networks has received attention in both research and actual applications. The prevalence of online social networking sites offers the possibility of mining for necessary information. However, existing influence maximization algorithms and newly proposed influence diffusion models do not distinguish between seed nodes (or pilot users) and nonseed nodes and assume all nodes are cooperative in propagating influence. This thesis investigates models and heuristics for noncooperative information diffusion in online social networks. It consists of three parts: tragedy of the commons in online social search (OSS), influence maximization in noncooperative social networks under the linear threshold model (LTM), and influence maximization in noncooperative social networks under the independent cascade model (ICM).
Firstly, the tragedy of the commons problem in OSS is considered. I propose an analytical model that captures the behavior of OSS nodes, and, from a gaming-strategy point of view, analyze various strategies an individual node can utilize to allocate its awareness capacity. Based on this I derive the Pareto inefficiency in terms of the system cost. An incentive scheme which can lead selfish nodes to the “social optimal” state of the whole system is also proposed. Extensive simulations show that the strategy with our proposed incentive mechanism outperforms other strategies in terms of the system cost and the search success rate. The second part of the thesis presents the first detailed analysis of influence maximization in noncooperative social networks under the LTM. The influence propagation process is structured into two stages, namely, seed node selection and influence diffusion. In the former, I introduce a generalized maximum-flow-based analytical framework to model the noncooperative behavior of individual users and develop a new seed node selection strategy. In the latter, I propose a game-theoretic model to characterize the behavior of noncooperative nodes and design a Vickrey-Clarke-Groves-like (VCG-like) scheme to incentivise cooperation. Then I study the budget allocation problem between the two stages, and show that a marketer can utilize the two proposed strategies to tackle noncooperation intelligently. The proposed schemes are evaluated on large coauthorship networks, and the results show that the proposed seed node selection scheme is very robust to noncooperation and the VCG-like scheme can effectively stimulate a node to become cooperative.
Finally, I study the influence maximization problem in noncooperative social networks under the ICM using the same two-stage framework originally proposed for LTM. For the seed selection stage, a modified hierarchy-based seed node selection strategy which can take node noncooperation into consideration is introduced. The VCG-like incentive scheme designed for the influence diffusion stage under LTM can also be utilized for ICM in a similar manner. Then I also study the budget allocation problem between the two stages. The evaluation results show that the performance of the hierarchy-based seed node selection scheme is satisfactory in a noncooperative social network and the VCG-like scheme can effectively encourage node cooperation.published_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy
7
Marchenko, Maria. "Endogenous Shocks in Social Networks: Exam Failures and Friends' Future Performance." WU Vienna University of Economics and Business, 2019. http://epub.wu.ac.at/7100/1/wp292.pdf.
Full textAbstract:
Exam failures of the students in a specific network may influence not only the future performance of the student but also all students from their friendship networks, affecting the overall cohort's performance. Therefore, it is crucial to understand how the whole network responses to failure. The difficulty of such analysis is incorporated in the probability of the failures being highly endogenous. In this paper, I am applying the novel identification and estimation approach to deal with such endogeneity. I am exploring the dynamic data on the students' networks in HSE, Nizhniy Novgorod. The results suggest that, on average, the exam failure of the friend have a negative effect on future performance.Series: Department of Economics Working Paper Series
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Louzada, Pinto Julio Cesar. "Information diffusion and opinion dynamics in social networks." Thesis, Evry, Institut national des télécommunications, 2016. http://www.theses.fr/2016TELE0001/document.
Full textAbstract:
La dissémination d'information explore les chemins pris par l'information qui est transmise dans un réseau social, afin de comprendre et modéliser les relations entre les utilisateurs de ce réseau, ce qui permet une meilleur compréhension des relations humaines et leurs dynamique. Même si la priorité de ce travail soit théorique, en envisageant des aspects psychologiques et sociologiques des réseaux sociaux, les modèles de dissémination d'information sont aussi à la base de plusieurs applications concrètes, comme la maximisation d'influence, la prédication de liens, la découverte des noeuds influents, la détection des communautés, la détection des tendances, etc. Cette thèse est donc basée sur ces deux facettes de la dissémination d'information: nous développons d'abord des cadres théoriques mathématiquement solides pour étudier les relations entre les personnes et l'information, et dans un deuxième moment nous créons des outils responsables pour une exploration plus cohérente des liens cachés dans ces relations. Les outils théoriques développés ici sont les modèles de dynamique d'opinions et de dissémination d'information, où nous étudions le flot d'informations des utilisateurs dans les réseaux sociaux, et les outils pratiques développés ici sont un nouveau algorithme de détection de communautés et un nouveau algorithme de détection de tendances dans les réseaux sociauxOur aim in this Ph. D. thesis is to study the diffusion of information as well as the opinion dynamics of users in social networks. Information diffusion models explore the paths taken by information being transmitted through a social network in order to understand and analyze the relationships between users in such network, leading to a better comprehension of human relations and dynamics. This thesis is based on both sides of information diffusion: first by developing mathematical theories and models to study the relationships between people and information, and in a second time by creating tools to better exploit the hidden patterns in these relationships. The theoretical tools developed in this thesis are opinion dynamics models and information diffusion models, where we study the information flow from users in social networks, and the practical tools developed in this thesis are a novel community detection algorithm and a novel trend detection algorithm. We start by introducing an opinion dynamics model in which agents interact with each other about several distinct opinions/contents. In our framework, agents do not exchange all their opinions with each other, they communicate about randomly chosen opinions at each time. We show, using stochastic approximation algorithms, that under mild assumptions this opinion dynamics algorithm converges as time increases, whose behavior is ruled by how users choose the opinions to broadcast at each time. We develop next a community detection algorithm which is a direct application of this opinion dynamics model: when agents broadcast the content they appreciate the most. Communities are thus formed, where they are defined as groups of users that appreciate mostly the same content. This algorithm, which is distributed by nature, has the remarkable property that the discovered communities can be studied from a solid mathematical standpoint. In addition to the theoretical advantage over heuristic community detection methods, the presented algorithm is able to accommodate weighted networks, parametric and nonparametric versions, with the discovery of overlapping communities a byproduct with no mathematical overhead. In a second part, we define a general framework to model information diffusion in social networks. The proposed framework takes into consideration not only the hidden interactions between users, but as well the interactions between contents and multiple social networks. It also accommodates dynamic networks and various temporal effects of the diffusion. This framework can be combined with topic modeling, for which several estimation techniques are derived, which are based on nonnegative tensor factorization techniques. Together with a dimensionality reduction argument, this techniques discover, in addition, the latent community structure of the users in the social networks. At last, we use one instance of the previous framework to develop a trend detection algorithm designed to find trendy topics in a social network. We take into consideration the interaction between users and topics, we formally define trendiness and derive trend indices for each topic being disseminated in the social network. These indices take into consideration the distance between the real broadcast intensity and the maximum expected broadcast intensity and the social network topology. The proposed trend detection algorithm uses stochastic control techniques in order calculate the trend indices, is fast and aggregates all the information of the broadcasts into a simple one-dimensional process, thus reducing its complexity and the quantity of necessary data to the detection. To the best of our knowledge, this is the first trend detection algorithm that is based solely on the individual performances of topics
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Alemayehu, Atsede Ghidey <1986>. "Essays on social networks, altruism and information diffusion." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9873/1/Atsede%20Ghidey%20Alemayehu_PhD%20Dissertation%282021%29%20Social%20Networks%20Altruism%20and%20Information%20Diffusion-final%20version.pdf.
Full textAbstract:
This dissertation consists of three standalone articles that contribute to the economics literature concerning technology adoption, information diffusion, and network economics in one way or another, using a couple of primary data sources from Ethiopia.
The first empirical paper identifies the main behavioral factors affecting the adoption of brand new (radical) and upgraded (incremental) bioenergy innovations in Ethiopia. The results highlight the importance of targeting different instruments to increase the adoption rate of the two types of innovations.
The second and the third empirical papers of this thesis, use primary data collected from 3,693 high school students in Ethiopia, and shed light on how we should select informants to effectively and equitably disseminate new information, mainly concerning environmental issues. There are different well-recognized standard centrality measures that are used to select informants. These standard centrality measures, however, are based on the network topology---shaped only by the number of connections---and fail to incorporate the intrinsic motivations of the informants. This thesis introduces an augmented centrality measure (ACM) by modifying the eigenvector centrality measure through weighting the adjacency matrix with the altruism levels of connected nodes.
The results from the two papers suggest that targeting informants based on network position and behavioral attributes ensures more effective and equitable (gender perspective) transmission of information in social networks than selecting informants on network centrality measures alone. Notably, when the information is concerned with environmental issues.
10
Marchenko, Maria. "Dealing with Endogenous Shocks in Dynamic Friendship Network." WU Vienna University of Economics and Business, 2019. http://epub.wu.ac.at/7099/1/wp291.pdf.
Full textAbstract:
Different types of shocks, or the treatment of one of the players in a specific network, may influence not only the future performance of themselves but also affect their network connections. It is crucial to explore the behaviour of the whole network in response to such an event. This paper focuses on the cases of endogenously formed shock. The logic used in the peer effect literature is adopted to develop the dynamic model and accounts for the endogeneity of the shock. The model allows us to predict the endogenous part of the shock and use the remaining unexpected component to estimate the effect of the shock on the changes in the performance of network connections. The identification conditions for effect are derived, and the consistent estimation procedure is proposed.Series: Department of Economics Working Paper Series
11
Weng, Huibin. "A Social Interaction Model with Endogenous Network Formation." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin159317152899108.
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12
Niu, Guolin, and 牛国林. "Temporal modeling of information diffusion in online social networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206478.
Full textAbstract:
The rapid development of online social networks (OSNs) renders them a powerful platform for information diffusion on a massive scale. OSNs generate enormous propagation traces. An important question is how to model the real-world information diffusion process. Although considerable studies have been conducted in this field, the temporal characteristics have not been fully addressed yet. This thesis addresses the issue of modeling the temporal dynamics of the information diffusion process. Based on empirical findings drawn from large-scale propagation traces of a popular OSN in China, we demonstrate that the temporal characteristics has a significant impact on the diffusion dynamics. Hence, a series of new temporal information diffusion models have been proposed by incorporating these temporal features. Experimental results demonstrate that these proposed models are more accurate and practical than existing discrete diffusion models. Moreover, one application of information diffusion models, i.e., the revenue maximization problem, is studied. Specifically, the thesis consists of three major parts: 1) preliminaries, i.e., introduction of research platform and collected dataset, 2) modeling social influence diffusion from three different temporal aspects, and 3) monetizing OSNs through designing intelligent pricing strategies in the diffusion process to realize the goal of revenue maximization.
Firstly, the research platform is introduced and the statistical properties of the data derived from this platform are investigated. We choose Renren, the dominant social network website in China, as our research platform and study its information propagation mechanisms. Specifically, we concentrate on the propagation of “sharing video” behaviors, and collect data on more than 2.8 million Renren users and over 209 million diffusion traces. The analysis result shows that the video access patterns in OSNs differ significantly from Youtube-like systems, which makes understanding the video propagation behaviors in OSNs an important research task.
Secondly, the temporal modeling of information diffusion is explored. By investigating temporal features using real diffusion traces, we find that three factors should be considered in building realistic diffusion models, including, information propagation latency, multiple influential sources and user diversities. We then develop models to explain the information propagation process by incorporating these factors, and demonstrate that the models reflect reality well.
Finally, revenue maximization in the information diffusion process is studied. Specifically, the pricing factor is explicitly incorporated into the product diffusion process. To realize the goal of revenue maximization, we develop a Dynamic Programming Based Heuristic (DPBH) to obtain the optimal pricing sequence. Application of the DPBH in the revenue maximization problem shows that it performs well in both the expected revenue achieved and in running time. This leads to fundamental ramifications to many related OSN marketing applications.published_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy
13
Houghton, James P. Ph D. Massachusetts Institute of Technology. "Why meaning matters for belief diffusion in social networks." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/124581.
Full textAbstract:
Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, 2019Cataloged from PDF version of thesis.
Includes bibliographical references (pages 13-14).
It is well known that human beings preferentially adopt beliefs that are consistent with what they already know (1). At its best, this process helps knowledge cumulate, and at its worst facilitates motivated reasoning and pseudoscience. Recent research in social contagion shows that the tendency to treat new information in light of what is already known creates interdependence in the diffusion patterns of simultaneously diffusing beliefs, and that this is sufficient to generate societal polarization and competing worldviews (2-8). This paper explains the mechanisms by which interdependence between beliefs can lead to fundamentally different patterns of adoption than would have occurred under traditional assumptions of independent diffusion. First, when beliefs facilitate one another's adoption, they spread to more individuals than any could have reached spreading on its own. Secondly, as individuals become more alike, they increase their likelihood of exchanging beliefs in the future, and of forming around themselves a faction of like-minded peers. These mechanisms explain why the most popular beliefs tend to be related to one another, and how polarization may spontaneously emerge in homogeneous and well-connected populations. Simulations in this paper make a direct comparison between interdependent and independent diffusion, explaining why the mechanisms of interdependent diffusion reverse many predictions of standard (independent) diffusion models. For example, while independently diffusing beliefs can make a population more homogenous, interdependent diffusion leads the same population to polarize. While the most successful independent beliefs are those with central network positions, interdependent beliefs become popular by facilitating the diffusion of related beliefs.
by James P. Houghton.
S.M. in Management Research
S.M.inManagementResearch Massachusetts Institute of Technology, Sloan School of Management
14
Monk, Adam Joel. "The Diffusion of New Music through Online Social Networks." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1337902485.
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Hoang, Thi Bich Ngoc. "Information diffusion, information and knowledge extraction from social networks." Thesis, Toulouse 2, 2018. http://www.theses.fr/2018TOU20078.
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La popularité des réseaux sociaux a rapidement augmenté au cours de la dernière décennie. Selon Statista, environ 2 milliards d'utilisateurs utiliseront les réseaux sociaux d'ici janvier 2018 et ce nombre devrait encore augmenter au cours des prochaines années. Tout en gardant comme objectif principal de connecter le monde, les réseaux sociaux jouent également un rôle majeur dans la connexion des commerçants avec les clients, les célébrités avec leurs fans, les personnes ayant besoin d'aide avec les personnes désireuses d'aider, etc.. Le succès de ces réseaux repose principalement sur l'information véhiculée ainsi que sur la capacité de diffusion des messages dans les réseaux sociaux. Notre recherche vise à modéliser la diffusion des messages ainsi qu'à extraire et à représenter l'information des messages dans les réseaux sociaux. Nous introduisons d'abord une approche de prédiction de la diffusion de l'information dans les réseaux sociaux. Plus précisément, nous prédisons si un tweet va être re-tweeté ou non ainsi que son niveau de diffusion. Notre modèle se base sur trois types de caractéristiques: basées sur l'utilisateur, sur le temps et sur le contenu. Nous avons évalué notre modèle sur différentes collections correspondant à une douzaine de millions de tweets. Nous avons montré que notre modèle améliore significativement la F-mesure par rapport à l'état de l'art, à la fois pour prédire si un tweet va être re-tweeté et pour prédire le niveau de diffusion. La deuxième contribution de cette thèse est de fournir une approche pour extraire des informations dans les microblogs. Plusieurs informations importantes sont incluses dans un message relatif à un événement, telles que la localisation, l'heure et les entités associées. Nous nous concentrons sur l'extraction de la localisation qui est un élément primordial pour plusieurs applications, notamment les applications géospatiales et les applications liées aux événements. Nous proposons plusieurs combinaisons de méthodes existantes d'extraction de localisation dans des tweets en ciblant des applications soit orientées rappel soit orientées précision. Nous présentons également un modèle pour prédire si un tweet contient une référence à un lieu ou non. Nous montrons que nous améliorons significativement la précision des outils d'extraction de lieux lorsqu'ils se focalisent sur les tweets que nous prédisons contenir un lieu. Notre dernière contribution présente une base de connaissances permettant de mieux représenter l'information d'un ensemble de tweets liés à des événements. Nous combinons une collection de tweets de festivals avec d'autres ressources issues d'Internet pour construire une ontologie de domaine. Notre objectif est d'apporter aux utilisateurs une image complète des événements référencés au sein de cette collectionThe popularity of online social networks has rapidly increased over the last decade. According to Statista, approximated 2 billion users used social networks in January 2018 and this number is still expected to grow in the next years. While serving its primary purpose of connecting people, social networks also play a major role in successfully connecting marketers with customers, famous people with their supporters, need-help people with willing-help people. The success of online social networks mainly relies on the information the messages carry as well as the spread speed in social networks. Our research aims at modeling the message diffusion, extracting and representing information and knowledge from messages on social networks. Our first contribution is a model to predict the diffusion of information on social networks. More precisely, we predict whether a tweet is going to be diffused or not and the level of the diffusion. Our model is based on three types of features: user-based, time-based and content-based features. Being evaluated on various collections corresponding to dozen millions of tweets, our model significantly improves the effectiveness (F-measure) compared to the state-of-the-art, both when predicting if a tweet is going to be retweeted or not, and when predicting the level of retweet. The second contribution of this thesis is to provide an approach to extract information from microblogs. While several pieces of important information are included in a message about an event such as location, time, related entities, we focus on location which is vital for several applications, especially geo-spatial applications and applications linked to events. We proposed different combinations of various existing methods to extract locations in tweets targeting either recall-oriented or precision-oriented applications. We also defined a model to predict whether a tweet contains a location or not. We showed that the precision of location extraction tools on the tweets we predict to contain a location is significantly improved as compared when extracted from all the tweets.Our last contribution presents a knowledge base that better represents information from a set of tweets on events. We combined a tweet collection with other Internet resources to build a domain ontology. The knowledge base aims at bringing users a complete picture of events referenced in the tweet collection (we considered the CLEF 2016 festival tweet collection)
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Delre, Sebastiano Alessio. "The effects of social networks on innovation diffusion and market dynamics." [S.l. : Groningen : s.n. ; University Library Groningen] [Host], 2007. http://irs.ub.rug.nl/ppn/304001422.
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Chen, Zhuo. "An Agent-Based Model for Information Diffusion Over Online Social Networks." Kent State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=kent1475760015492269.
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Cheng, Jiesi. "Information Diffusion and Influence Propagation on Social Networks with Marketing Applications." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/306134.
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Web and mobile technologies have had such profound impact that we have witnessed significant evolutionary changes in our social, economic and cultural activities. In recent years, online social networking sites such as Twitter, Facebook, Google+, and LinkedIn have gained immense popularity. Such social networks have led to an enormous explosion of network-centric data in a wide variety scenarios, posing unprecedented analytical and computational challenges to MIS researchers. At the same time, the availability of such data offers major research opportunities in various social computing and analytics areas to tackle interesting questions such as: - From a business and marketing perspective, how to mine the novel datasets of online user activities, interpersonal communications and interactions, for developing more successful marketing strategies? - From a system development perspective, how to incorporate massive amounts of available data to assist online users to find relevant, efficient, and timely information? In this dissertation, I explored these research opportunities by studying multiple analytics problems arose from the design and use of social networking services. The first two chapters (Chapter 2 and 3) are intended to study how social network can help to derive a better estimation of customer lifetime value (CLV), in the social gaming context. In Chapter 2, I first conducted an empirical study to demonstrate that friends' activities can serve as significant indicators of a player's CLV. Based on this observation, I proposed a perceptron-based online CLV prediction model considering both individual and friendship information. Preliminary results have shown that the model can be effectively used in online CLV prediction, by evaluating against other commonly-used benchmark methods. In Chapter 3, I further extended the metric of traditional CLV, by incorporating the personal influences on other customers' purchase as an integral part of the lifetime value. The proposed metric was illustrated and tested on seven social games of different genres. The results showed that the new metric can help marketing managers to achieve more successful marketing decisions in user acquisition, user retention, and cross promotion. Chapter 4 is devoted to the design of a recommendation system for micro-blogging. I studied the information diffusion pattern in a micro-blogging site (Twitter.com) and proposed diffusion-based metrics to assess the quality of micro-blogs, and leverage the new metric to implement a novel recommendation framework to help micro-blogging users to efficiently identify quality news feeds. Chapter 5 concludes this dissertation by highlighting major research contributions and future directions.
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Bao, Qing. "Inferring diffusion models with structural and behavioral dependency in social networks." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/305.
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Online social and information networks, like Facebook and Twitter, exploit the influence of neighbors to achieve effective information sharing and spreading. The process that information is spread via the connected nodes in social and information networks is referred to as diffusion. In the literature, a number of diffusion models have been proposed for different applications like influential user identification and personalized recommendation. However, comprehensive studies to discover the hidden diffusion mechanisms governing the information diffusion using the data-driven paradigm are still lacking. This thesis research aims to design novel diffusion models with the structural and behaviorable dependency of neighboring nodes for representing social networks, and to develop computational algorithms to infer the diffusion models as well as the underlying diffusion mechanisms based on information cascades observed in real social networks. By incorporating structural dependency and diversity of node neighborhood into a widely used diffusion model called Independent Cascade (IC) Model, we first propose a component-based diffusion model where the influence of parent nodes is exerted via connected components. Instead of estimating the node-based diffusion probabilities as in the IC Model, component-based diffusion probabilities are estimated using an expectation maximization (EM) algorithm derived under a Bayesian framework. Also, a newly derived structural diversity measure namely dynamic effective size is proposed for quantifying the dynamic information redundancy within each parent component. The component-based diffusion model suggests that node connectivity is a good proxy to quantify how a node's activation behavior is affected by its node neighborhood. To model directly the behavioral dependency of node neighborhood, we then propose a co-activation pattern based diffusion model by integrating the latent class model into the IC Model where the co-activation patterns of parent nodes form the latent classes for each node. Both the co-activation patterns and the corresponding pattern-based diffusion probabilities are inferred using a two-level EM algorithm. As compared to the component-based diffusion model, the inferred co-activation patterns can be interpreted as the soft parent components, providing insights on how each node is influenced by its neighbors as reflected by the observed cascade data. With the motivation to discover a common set of the over-represented temporal activation patterns (motifs) characterizing the overall diffusion in a social network, we further propose a motif-based diffusion model. By considering the temporal ordering of the parent activations and the social roles estimated for each node, each temporal activation motif is represented using a Markov chain with the social roles being its states. Again, a two-level EM algorithm is proposed to infer both the temporal activation motifs and the corresponding diffusion network simultaneously. The inferred activation motifs can be interpreted as the underlying diffusion mechanisms characterizing the diffusion happening in the social network. Extensive experiments have been carried out to evaluate the performance of all the proposed diffusion models using both synthetic and real data. The results obtained and presented in the thesis demonstrate the effectiveness of the proposed models. In addition, we discuss in detail how to interpret the inferred co-activation patterns and interaction motifs as the diffusion mechanisms under the context of different real social network data sets.
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Pyo, Tae-Hyung. "Three essays on social networks and the diffusion of innovation models." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/1383.
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The Bass model has been used extensively and globally to forecast the first purchases of new products. It has been named by INFORMS as one of the top 10 most influential papers published in the 50-year history of Management Science. Most models for the diffusion of innovation are deeply rooted in the work of Bass (1969). His work provides a framework to model the underlying process of innovation adaption among first-time customers.
Potential customers may be connected to one another in some sort of network. Prior research has shown that the structure of a network affects adoption patterns (Dover et al. 2012; Hill et al. 2006; Katona and Sarvary 2008; Katona et al. 2011; Newman et al. 2006; Shaikh et al. 2010; Van den Bulte and Joshi 2007). One approach to addressing this issue is to incorporate network information into the original Bass model. The focus of this study is to explore how to incorporate network information and other micro-level data into the Bass model.
First, I prove that the Bass Model assumes all potential customers are linked to all other customers. Through simulations of individual adoptions and connections among individuals using a Random Network , I show that the estimate of q in the Bass Model is biased downward in the original Bass model. I find that biases in the Bass Model depend on the structure of the network. I relax the assumption of the fully connected network by proposing a Network-Based Bass model (NBB), which incorporates the network structure into the traditional Bass model. Using the proposed model (NBB), I am able to recover the true parameters.
To test the generalizability and to enhance the applicability of my NBB model, I tested my NBB model on the various network types with sampled data from the population network. I showed that my NBB model is robust across different types of networks, and it is efficient in terms of sample size. With a small fraction of data from the population, it accurately recovered the true parameters. Therefore, the NBB model can be used when we do not have complete network information.
The last essay is the first attempt to incorporate heterogeneous peer influence into the NBB model, based on individuals' preference structures. Besides the significant extension of the NBB (Bass) Model, incorporating high-quality data on individual behavior into the model leads to new findings on individuals' adoption behaviors, and thus expands our knowledge of the diffusion process.
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Wang, Chunlei. "Social networks, competition, and institutionalization : an integrated perspective on social influence in innovation diffusion /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
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Delladio, Eleonora <1996>. "Empowering Models of Diffusion of Innovation through Social Networks and their Agents." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/19803.
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Nowadays, our world is offering a huge amount of data and tools to gather useful information for various marketing purposes. The aim is to give a new and contemporary dimension to the theory and models of diffusion of innovation of the twentieth century, by exploiting the potential given by the possible analysis of the interconnections between the agents within social networks. Interactions and influences between consumers have a strong impact on the decisional process; consequently, the patterns and dynamics of social networks should be deeply understood. Agents are classified according to specific criteria: responses to innovation, position within the network, potential influences on other agents, extension and density of the network. An overview of the available theoretical tools will be put in place to identify the paths undertaken to achieve the goal of this script and perform additional forecasts to products’ life cycle, sales trends, and speed of diffusion of innovation. The company “Northwave”, based in the region of Veneto in Italy, is discovered and analyzed to give a concrete example of the application and exploitation of the models with the addition of social networks’ dynamics. Analytical instruments and methods are the cornerstone to reach the scope, in addition to academic books and more recent articles.
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Coad, Bethany. "Neurocognitive networks for social cognition : insights from diffusion weighted imaging and frontotemporal dementia." Thesis, Cardiff University, 2017. http://orca.cf.ac.uk/111503/.
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Empathy is a complex and multicomponent social cognitive function. It is underpinned by large-scale neurocognitive networks, the precise cognitive and neural structure of which remains debated. Despite this, relatively little work has considered the cognitive or neural components of empathy at the network-level. Here I present work using diffusion weighted magnetic resonance imaging (DWI) in healthy adults, and cognitive and behavioural assessment in a relatively rare form of dementia, the behavioural variant of frontotemporal dementia (bvFTD). Using these methods I explore: (a) the relationship between the microstructural properties of white matter tracts that mediate connectivity between distinct neurocognitive networks and separable cognitive components of empathic cognition (b) the cognitive and behavioural consequences of perturbation to neurocognitive networks in dementia. BvFTD is of interest here as it appears to preferentially target neural networks that support socioemotional processing. In chapters 2 and 3, evidence regarding the white matter structures that are affected by bvFTD guides investigations of the relationship between the microstructural properties of specific white matter tracts and social cognitive functioning in the healthy adult brain. In these chapters, I show that, in young healthy adults, two white matter pathways, sensitive to early changes in bvFTD, the Uncinate fasciculus (UF) and the cingulum bundle (CB), are related to individual differences in two components of empathic functioning, respectively: facial emotion decoding and mentalising. In chapter 4 I show the dissociation of performance on tasks assessing these cognitive functions in an individual with early bvFTD. I highlight the sensitivity and potential clinical utility of tasks assessing literary fiction-based mentalising. In Chapter 5 I present a detailed qualitative description of social cognitive change in frontotemporal dementia (FTD), from the perspective of family members. I consider what such detailed descriptions of everyday behaviour may tell us about the cognitive underpinnings of complex social behaviour. The findings of this thesis further our understanding of the dissociable neurocognitive networks that support empathic functioning, including their structural underpinnings and the behavioural consequences of their perturbation. In the general discussion, I consider the implications of this work for our understanding of social cognitive functioning and bvFTD.
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Jiang, Shan. "Statistical Modeling of Multi-Dimensional Knowledge Diffusion Networks: An ERGM-Based Framework." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/555946.
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Knowledge diffusion networks consist of individuals who exchange knowledge and knowledge flows connecting the individuals. By studying knowledge diffusion in a network perspective, it helps us understand how the connections between individuals affect the knowledge diffusion processes. Existing research on knowledge diffusion networks mostly adopts a uni-dimensional perspective, where all the individuals in the networks are assumed to be of the same type. It also assumes that there is only one type of knowledge flow in the network. This dissertation proposes a multi-dimensional perspective of knowledge diffusion networks and examines the patterns of knowledge diffusion with Exponential Random Graph Model (ERGM) based approaches. The objective of this dissertation is to propose a framework that effectively addresses the multi-dimensionality of knowledge diffusion networks, to enable researchers and practitioners to conceptualize the multi-dimensional knowledge diffusion networks in various domains, and to provide implications on how to stimulate and control the knowledge diffusion process. The dissertation consists of three essays, all of which examine the multi-dimensional knowledge diffusion networks in a specific context, but each focuses on a different aspect of knowledge diffusion. Chapter 2 focuses on how structural properties of networks affect various types of knowledge diffusion processes in the domain of commercial technology. The study uses ERGM to simultaneously model multiple types of knowledge flows and examine their interactions. The objective is to understand the impacts of network structures on knowledge diffusion processes. Chapter 3 focuses on examining the impact of individual attributes and the attributes of knowledge on knowledge diffusion in the context of scientific innovation. Based on social capital theory, the study also utilizes ERGM to examine how knowledge transfer and knowledge co-creation can be affected by the attributes of individual researchers and the attributes of scientific knowledge. Chapter 4 considers the dynamic aspect of knowledge diffusion and proposes a novel network model extending ERGM to identify dynamic patterns of knowledge diffusion in social media. In the proposed model, dynamic patterns in social media networks are modeled based on the nodal attributes of individuals and the temporal information of network ties.
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McGlohon, Mary. "Structural Analysis of Large Networks: Observations and Applications." Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/18.
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Network data (also referred to as relational data, social network data, real graph data) has become ubiquitous, and understanding patterns in this data has become an important research problem. We investigate how interactions in social networks are formed and how these interactions facilitate diffusion, model these behaviors, and apply these findings to real-world problems.
We examined graphs of size up to 16 million nodes, across many domains from academic citation networks, to campaign contributions and actor-movie networks. We also performed several case studies in online social networks such as blogs and message board communities.
Our major contributions are the following: (a) We discover several surprising patterns in network topology and interactions, such as Popularity Decay power law (in-links to a blog post decay with a power law with -1:5 exponent) and the oscillating size of connected components; (b) We propose generators such as the Butterfly generator that reproduce both established and new properties found in real networks; (c) several case studies, including a proposed method of detecting misstatements in accounting data, where using network effects gave a significant boost in detection accuracy.
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Doo, Myungcheol. "Spatial and social diffusion of information and influence: models and algorithms." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44740.
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With the ubiquity of broadband, wireless and mobile networking and the diversity of user-driven social networks and social channels, we are entering an information age where people and vehicles are connected at all times, and information and influence are diffused continuously through not only traditional authoritative media such as news papers, TV and radio broadcasting, but also user-driven new channels for disseminating information and diffusing influence. Social network users and mobile travelers can influence and be influenced by the social and spatial connectivity that they share through an impressive array of social and spatial channels, ranging from friendship, activity, professional or social groups to spatial, location-aware, and mobility aware events.
In this dissertation research, we argue that spatial alarms and activity-based social networks are two fundamentally new types of information and influence diffusion channels. Such new channels have the potential of enriching our professional experiences and our personal life quality in many unprecedented ways. For instance, spatial alarms enable people to share their experiences or disseminate certain points of interest by leaving location-dependent greetings, tips or graffiti and location dependent tour guide to their friends, colleagues and family members. Through social networks, people can influence their friends and colleagues by the activities they have engaged, such as reviews and blogs on certain events or products. More interestingly, the power of such spatial and social diffusion of information and influence can go far beyond our physical reach. People can utilize user-generated social and spatial channels as effective means to disseminate information and propagate influence to a much wider and possibly unknown range of audiences and recipients at any time and in any location. A fundamental challenge in embracing such new and exciting ways of information diffusion is to develop effective and scalable models and algorithms as enabling technology and building blocks. This dissertation research is dedicated towards this ultimate objective with three novel and unique contributions.
First, we develop an activity driven and self-configurable social influence model and a suite of computational algorithms to compute and rank social network nodes in terms of activity-based influence diffusion over social network topologies. By activity driven we mean that the real impact of social influence and the speed of such influence propagation should be computed based on the type, the amount and the time window of the activities performed by a social network node in addition to its social connectivity (social network topology). By self-configurable we mean that the diffusion efficiency and effectiveness are dynamically adapted based on the settings and tunings of multiple spatial and social parameters such as diffusion context, diffusion location, diffusion rate, diffusion energy (heat), diffusion coverage and diffusion incentives (e.g., reward points), to name a few. We evaluate our approach through datasets collected from Facebook, Epinions, and DBLP datasets. Our experimental results show that our activity based social influence model outperforms existing topology-based social influence model in terms of effectiveness and quality with respect to influence ranking and influence coverage computation.
Second, we further enhance our activity based social influence model along two dimensions. At first, we use a probabilistic diffusion model to capture the intrinsic properties of social influence such that nodes in a social network may have the choice of whether to participate in a social influence propagation process. We examine threshold based approach and independent probabilistic cascade based approach to determine whether a node is active or inactive in each round of influence diffusion. Secondly, we introduce incentives using multi-scale reward points, which are popularly used in many business settings. We then examine the effectiveness of reward points based incentives in stimulating the diffusion of social influences. We show that given a set of incentives, some active nodes may become more active whereas some inactive nodes may become active. Such dynamics changes the composition of the top-k influential nodes computed by activity-based social influence model. We make several interesting observations: First, popular users who are high degree nodes and have many friends are not necessarily influential in terms of spawning new activities or spreading ideas and information. Second, most influential users are more active in terms of their participation in the social activities and interactions with their friends in the social network. Third, multi-scale reward points based incentives can be effective to both inactive nodes and active nodes.
Third, we introduce spatial alarms as the basic building blocks for location-dependent information sharing and influence diffusion. People can share and disseminate their location based experiences and points of interest to their friends and colleagues in the form of spatial alarms. Spatial alarms are triggered and delivered to the intended subscribers only when the subscribers move into the designated geographical vicinity of the spatial alarms, enabling delivering and sharing of relevant information and experience at the right location and the right time with the right subscribers. We studied how to use locality filters and subscriber filers to enhance the spatial alarm processing using traditional spatial indexing techniques. In addition, we develop a fast spatial alarm indexing structure and algorithms, called Mondrian Tree, and demonstrate that the Mondrian tree enabled spatial alarm system can significantly outperform existing spatial indexing based solutions such as R-tree, $k$-d tree, Quadtree.
This dissertation consists of six chapters. The first chapter introduces the research hypothesis. We describe our activity-based social influence model in Chapter 2. Chapter 3 presents the probabilistic social influence model powered with rewards incentives. We introduce spatial alarms and the basic system architecture for spatial alarm processing in Chapter 4. We describe the design of our Mondrian tree index of spatial alarms and alarm free regions in Chapter 5. In Chapter 6 we conclude the dissertation with a summary of the unique research contributions and a list of open issues closely relevant to the research problems and solution approaches presented in this dissertation.
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Zuo, Xiang. "The Role of Social Ties in Dynamic Networks." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6160.
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Social networks are everywhere, from face-to-face activities to online social networks such as Flickr, YouTube and Facebook. In social networks, ties (relationships) are connections between people. The change of social relationships over time consequently leads to the evolution of the social network structure. At the same time, ties serve as carriers to transfer pieces of information from one person to another.
Studying social ties is critical to understanding the fundamental processes behind the network. Although many studies on social networks have been carried out over the last many decades, most of the work either used small in-lab datasets, or focused on directly connected static relations while ignoring indirect relations and the dynamic nature of real networks. Today, because of the emergence of online social networks, more and more large longitudinal social datasets are becoming available. The available real social datasets are fundamental to understanding evolution processes of networks in more depth. In this thesis, we study the role of social ties in dynamic networks using datasets from various domains of online social networks.
Networks, especially social networks often exhibit dual dynamic nature: the structure of the graph changes (by node and edge insertion and removal), and information flows in the network. Our work focuses on both aspects of network dynamics. The purpose of this work is to better understand the role of social ties in network evolution and changes over time, and to determine what social factors help shape individuals’ choices in negative behavior. We first developed a metric that measures the strength of indirectly connected ties. We validated the accuracy of the measurement of indirect tie metric with real-world social datasets from four domains.
Another important aspect of my research is the study of edge creation and forecast future graph structure in time evolving networks. We aim to develop algorithms that explain the edge formation properties and process which govern the network evolution. We also designed algorithms in the information propagation process to identify next spreaders several steps ahead, and use them to predict diffusion paths.
Next, because different social ties or social ties in different contexts have different influence between people, we looked at the influence of social ties in behavior contagion, particularly in a negative behavior cheating. Our recent work included the study of social factors that motivate or limit the contagion of cheating in a large real-world online social network. We tested several factors drawn from sociology and psychology explaining cheating behavior but have remained untested outside of controlled laboratory experiments or only with small, survey based studies.
In addition, this work analyzed online social networks with large datasets that certain inherent influences or patterns only emerge or become visible when dealing with massive data. We analyzed the world’s largest online gaming community, Steam Community, collected data with 3, 148, 289 users and 44, 725, 277 edges. We also made interesting observations of cheating influence that were not observed in previous in-lab experiments.
Besides providing empirically based understanding of social ties and their influence in evolving networks at large scales, our work has high practical importance for using social influence to maintain a fair online community environment, and build systems to detect, prevent, and mitigate undesirable influence.
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Vallet, Jason. "Where Social Networks, Graph Rewriting and Visualisation Meet : Application to Network Generation and Information Diffusion." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0818/document.
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Dans cette thèse, nous présentons à la fois une collection de modèles de générations de réseaux et de diffusion d'information exprimés à l'aide d'un formalisme particulier appelé la réécriture de graphes, ainsi qu'une nouvelle méthode de représentation permettant la visualisation de la diffusion d'information dans des grands réseaux sociaux. Les graphes sont des objets mathématiques particulièrement versatiles qui peuvent être utilisés pour représenter une large variété de systèmes abstraits. Ces derniers peuvent être transformés de multiples façons (création, fusion ou altération de leur éléments), mais de telles modifications doivent être contrôlées afin d'éviter toute opération non souhaitée. Pour cela, nous faisons appel au formalisme particulier de la réécriture de graphes afin d'encadrer et de contrôler toutes les transformations. Dans notre travail, un système de réécriture de graphes opère sur un graphe, qui peut être transformé suivant un ensemble de règles, le tout piloté par une stratégie. Nous commençons tout d'abord par utiliser la réécriture en adaptant deux algorithmes de génération de réseaux, ces derniers permettant la création de réseaux aux caractéristiques petit monde. Nous traduisons ensuite vers le formalisme de réécriture différents modèles de diffusion d'information dans les réseaux sociaux. En énonçant à l'aide d'un formalisme commun différents algorithmes, nous pouvons plus facilement les comparer, ou ajuster leurs paramètres. Finalement, nous concluons par la présentation d'un nouvel algorithme de dessin compact de grands réseaux sociaux pour illustrer nos méthodes de propagation d'informationIn this thesis, we present a collection of network generation and information diffusion models expressed using a specific formalism called strategic located graph rewriting, as well as a novel network layout algorithm to show the result of information diffusion in large social networks. Graphs are extremely versatile mathematical objects which can be used to represent a wide variety of high-level systems. They can be transformed in multiple ways (e.g., creating new elements, merging or altering existing ones), but such modifications must be controlled to avoid unwanted operations. To ensure this point, we use a specific formalism called strategic graph rewriting. In this work, a graph rewriting system operates on a single graph, which can then be transformed according to some transformation rules and a strategy to steer the transformation process. First, we adapt two social network generation algorithms in order to create new networks presenting small-world characteristics. Then, we translate different diffusion models to simulate information diffusion phenomena. By adapting the different models into a common formalism, we make their comparison much easier along with the adjustment of their parameters. Finally, we finish by presenting a novel compact layout method to display overviews of the results of our information diffusion method
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Vyas, Amit. "Adoption, use and diffusion of online social networks in the older population : a UK perspective." Thesis, University of Hertfordshire, 2013. http://hdl.handle.net/2299/12847.
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Since households and businesses alike obtained the high-speed Internet service of broadband, the Internet has become integral to daily life in the 21st century. Advancements in information and Internet technology has led to the conception of novel internet- enabled applications such as, Online Social Networks (OSNs). Since the turn of the twenty first century fast-developing OSNs such as, Twitter and Facebook have become essential communication channels that people are using to develop their online personal and professional networks online. A recent phenomenon that is worrying countries around the globe is an ageing population. Due to recent improvements in the quality of life and advances in medicine, individuals are achieving longer life spans. Given the fact that older adults are also experiencing loneliness and depression, a recent solution to reduce this problem is the use of OSNs. Using these reasons as motivation, the aim of this research is to identify and understand the factors driving or inhibiting the adoption, use and diffusion of OSNs within the older population (50+) in UK households. In order to achieve this aim the Model of Online Social Networking (MOSN) was conceptually developed. Drawing upon the attitudinal, normative and control constructs from the leading Information Systems (IS) theories of the Diffusion of Innovations theory (DOI), Theory of Planned Behavior (TPB), Model of Adoption of Technology in Households (MATH) and the E-Services Adoption Model selected constructs were identified and formed. To achieve the aim, the conceptual framework (MOSN – Model of Online Social Networking) was initially empirically validated using primary data. A quantitative approach involving a small-scale online pilot survey (n-252) and a wide-scale online survey (n-1080) were used for this purpose. Findings revealed that that older individuals will adopt Internet technologies if technology-facilitating conditions such as ‘anytime access’ to Internet capable devices and a fast reliable Internet connection had significant positive effects on OSN intention. In terms of influences of peers, it was revealed that older individuals do consider and act upon the views of members in one’s social circle. Most significantly, the consequences of older adults efforts to preserve their own privacy enforces a vast majority of non-adopters from not taking part in the OSN uptake. In terms of diffusion it was found that messages about OSNs conveyed through media channels: TV, newspapers and magazines are having a negative impact on older adults intention to adopt OSNs. As little is known of the underlying factors effecting older individuals adoption or non-adoption and diffusion of OSNs this research contributes to an emerging body of knowledge through the identification of empirically supported factors found to be significantly influencing UK older adults decision making regarding OSN technology adoption. For those participants currently using OSNs an in-depth understanding of usage behavior is presented. Importantly this research addresses a gap in research relating to the household adoption of OSNs in older adults in the UK. Due to the limitations of time, finance and manpower research findings could not be nationally representative of the UK are only representative of a single group of society residing in an affluent area of the UK.
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Murray, Deborah Adkins. "ORGANIZATIONAL ADAPTATION THROUGH DIFFUSION AND SOCIAL NETWORKS: A STUDY OF FAMILY CONSUMER SCIENCES EXTENSION AGENTS." UKnowledge, 2012. http://uknowledge.uky.edu/edl_etds/2.
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This study examines the interconnectedness of social networks of the early adopter Family and Consumer Science Extension Agents (FCS Agents) of the Mental Healthiness and Aging Initiative (MHAI) pilot conducted in eleven (11) eastern Kentucky counties between October 2007 and April 2009 and compares the social network connections of the FCS Agents in the other six Extension Districts in Kentucky.
This research used whole-network survey analysis applying the social network approach, a conceptual model for explaining the communication of new ideas and information within an organizational network. Organizational networks are important structural elements of organizational systems and key to understanding diffusion of new programs within institutional organizations, such as the Kentucky Cooperative Extension Service.
Previous diffusion studies by Extension scholars have concentrated on the classic diffusion model of agricultural technology innovations with individual farmer adopters. Adoption of new programs and ideas is the process by which individuals in a social system decide to use the communicated new idea, program, and/or technology. This conceptual model describes the stages of diffusion through the attributes of the clientele adopters. The social network conceptual model describes diffusion through communication channels. Identified opinion leaders are matched with those who nominate them or closely identify with them in a diffusion network perspective to accelerate the diffusion process through an optimal pairing of network member with influencers.
Data were collected from the FCS Extension Agent network in an online survey “FCS Health Information Communication Network Survey” from July 1, 2011 – July 30, 2011. Participants were asked to rate each of their co-workers in their own district, and in each of the other six districts, on how often they go to each person directly for health education information. Hypothesis testing supports the use of opinion leaders, bridges and communication structures within the social network structure of FCS agents for diffusing health programming within the Cooperative Extension Service.
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Nordvik, Monica K. "Contagious Interactions : Essays on social and epidemiological networks." Doctoral thesis, Stockholm : Visby : Acta Universitatis Stockholmiensis ; eddy.se [distributör], 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-8309.
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Zhang, Jurui. "Social Learning in a World of Friends Versus Connected Strangers: A Theoretical Model with Experimental Evidence." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/228497.
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Networks and the relationships embedded in them are critical determinants of how people communicate, form beliefs, and behave. E-commerce platforms such as Amazon and eBay have made actions of "strangers" more observable to others. More recently, social media websites such as Facebook and Google Plus have created networks of "friends", and the actions of these friends have become more visible than ever before to consumers. This dissertation develops an analytical model to examine how social learning occurs in different types of networks. Specifically, I examine the pure-strategy perfect Bayesian equilibrium of observational learning in a friend-network vs. a stranger-network. In this model, each individual makes an adopt-or-reject decision about a product after receiving a private signal regarding the underlying quality of the product and observing past actions of other individuals in the network. Grounded on the homophily theory in sociology, the degree of network heterogeneity defines the key difference between a friend-network and a stranger-network. I show a threshold effect of network size regarding which network carries more valuable information: when the network size is small, a friend-network carries more valuable information than a stranger-network does. But when the network size gets larger, a stranger-network dominates a friend-network. This suggests two competing effects of network homogeneity on social learning: individual preference effects and social conforming effects. I also test key implications from theoretical results using experiments to demonstrate internal validity and enhance insights on social learning in networks. I found that experimental results are in line with predictions from the theoretical model.
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Khater, Shaymaa. "Personalized Recommendation for Online Social Networks Information: Personal Preferences and Location Based Community Trends." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/64283.
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Online social networks are experiencing an explosive growth in recent years in both the number of users and the amount of information shared. The users join these social networks to connect with each other, share, find content and disseminate information by sending short text messages in near realtime. As a result of the growth of social networks, the users are often experiencing information overload since they interact with many other users and read ever increasing content volume. Thus, finding the "matching" users and content is one of the key challenges for social networks sites. Recommendation systems have been proposed to help users cope with information overload by predicting the items that a user may be interested in.
The users' preferences are shaped by personal interests. At the same time, users are affected by their surroundings, as determined by their geographically located communities. Accordingly, our approach takes into account both personal interests and local communities. We first propose a new dynamic recommendation system model that provides better customized content to the user. That is, the model provides the user with the most important tweets according to his individual interests. We then analyze how changes in the surrounding environment can affect the user's experience. Specifically, we study how changes in the geographical community preferences can affect the individual user's interests. These community preferences are generally reflected in the localized trending topics. Consequently, we present TrendFusion, an innovative model that analyzes the trends propagation, predicts the localized diffusion of trends in social networks and recommends the most interesting trends to the user. Our performance evaluation demonstrate the effectiveness of the proposed recommendation system and shows that it improves the precision and recall of identifying important tweets by up to 36% and 80%, respectively. Results also show that TrendFusion
accurately predicts places in which a trend will appear, with 98% recall and 80% precision.Ph. D.
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Wang, Dong. "Analyse et application de la diffusion d'information dans les microblogs." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAA023/document.
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Les services de microblogging (comme Twitter ou Sina Weibo) sont devenu ces dernières années des plateformes très importantes de partage d'information sur l'Internet. Les microblogs sont fréquemment utilisé pour l'analyse de l'opinion, le marketing viral, et les campagnes politiques. Comprendre les mécanismes sous-jacents de la diffusion d'information sur les microblogs et comment des contenus deviennent populaires est important.L‘analyse de la diffusion d'information dans les microblogs nécessite la collecte de donnée des microblogs, la modélisation de la diffusion d'information et l'application des modèles résultants. Traiter les données massives issues des microblogs est un défi en soi. Concevoir des algorithmes efficaces et sans biais afin d'échantillonner les microblogs est ainsi fondamental. Ceci doit prendre en compte la complexité du phénomène de « retweet » qui dépend de la valeur éphémère de l'information, de la topologie du réseau de microblogging et des caractéristiques particulières des éditeurs et retweeteurs.Deux modèles ont été traditionnellement appliqués à la diffusion d'information : les cascades indépendantes et modèle à seuil linéaire. Aucun de ces deux modèles n'est à même de décrire le processus du retweeting de façon correcte. Il devient donc nécessaire de de caractériser la diffusion d'information. De plus, une description complète de la relation entre la diffusion d'information dans les microblogs et de popularité des termes recherchés sur Internet serait utile.Ces travaux de thèse présentent une analyse complète de la diffusion d'information dans les microblogs. Les contributions ce cette thèse sont les suivantes :1) Il y'a deux technique d'échantillonnage sans biais pour les réseaux sociaux : la marche aléatoire de Métropolis-Hastings (MHRW), et la méthode d'échantillonnage sans biais de graphes dirigés (USDSG). Néanmoins ces deux méthodes peuvent aboutit à un taux important d'auto-échantillonnage quand elles sont appliquées à des microblogs. Pour résoudre ce problème, j'ai modélisé l'échantillonnage d'un OSN par un processus de Markov et j'en ai déduit les conditions nécessaires et suffisantes d'un échantillonnage sans biais. Ces conditions m'ont permis de proposer un algorithme d'échantillonnage sans biais et efficace que j'ai nommé : échantillonnage sans biais par liens vide (USDE). Cette nouvelle méthode d'échantillonage réduit fortement l'auto-échantillonnage du MHRW. L ‘évaluation empirique montre que la moyenne des dégrées des nœuds échantillonnés est proche de la vérité terrain alors que pour MHRW et USDSG elle est 2 à 4 fois supérieure.2) La seconde contribution de cette thèse vise les lacunes des modèles en cascades indépendantes et de seuils linéaires. J'ai développé un modèle fondé sur les processus de Galton-Watson avec mort (GWK) qui prennent en compte tous les facteurs importants du processus de retweet. Ce nouveau modèle est validé par une application sur des données issues de Twitter et de Weibo.3) La troisième contribution est relative au développement d'un modèle économique du marché des acteurs actifs dans le domaine du marketing sur les mots clés dans les sites de recherches. J'ai développé des méthodes de gestion de portfolios de mots clés et montrés que ces portfolios permettent d'améliorer fortement les rendements sans augmenter le niveau de risqueMicroblog service (such as Twitter and Sina Weibo) have become an important platform for Internet content sharing. As the information in Microblog are widely used in public opinion mining, viral marketing and political campaigns, understanding how information diffuses over Microblogs, and explaining the process through which some tweets become popular, are important.The analysis of the information diffusion in Microblogs involves the data collection from Microblog, the modeling on information spreading and using the resulting models. Dealing with the huge amount of data flowing through microblogs is by itself a challenge. Designing an efficient and unbiased sampling algorithm for Microblog is therefore essential. Besides, the retweeting process in Microblog is complex because of the ephemerality of information, the topology of Microblog network and the particular features (such as number of followers) of publisher and retweeters.Two traditional models have been used for information diffusion : Independent Cascades and Linear Threshold models. However no one of them can describe completely the retweeting process in Microblog accurately. The analysis and design of new models to characterize the information diffusion in Microblog is therefore necessary. Moreover, a comprehensive description of the correlation between the information diffusion in Microblog and the searching trends of keywords on search engines is lacking although some work has been found some preliminary relationships.This work presnets a complete analysis of information diffusion in Microblog from. The contributions and innovations of this thesis are as follows:1)There are two popular unbiased Online Social Network (OSN) sampling algorithms,Metropolis-Hastings Random Walk (MHRW) and Unbiased Sampling for Directed Social Graph (USDSG) method. However they are both likely to yield considerable self-sampling probabilities when applied to Microblogs where there is local. To solve this problem, I have modelled the process of OSN sampling as a Markov process and have deduced the sufficient and necessary conditions of unbiased sampling. Based on this unbiased conditions, I proposed an efficient and unbiased sampling algorithms, Unbiased Sampling method with Dummy Edges (USDE), which reduces strongly the self-sampling probabilities of MHRW. The experimental evaluation demonstrate thats the average node degree of samples of MHRW and USDSG is 2 - 4 times as high as the ground truth while USDE can provide the approximation of ground truth when the sampling repetitions are removed. Moreover the average sampling time per node in USDE is only a half of MHRW and USDSG one.2)A second contribution targets the shortages of Independent Cascades (IC) and Linear Threshold (LT) models in characterizing the retweeting process in Microblogs. I achieve this by introducing a Galton Watson with Killing (GWK) model which considers all the three important factors including the ephemerality of information, the topology of network and the features of publisher and retweeters accurately. We have validated the applicability of the of GWK model over two datasets from Sina Weibo and Twitter and showed that GWK model can fit 82% of information receivers and 90% of the maximum numbers of hops in the real retweeting process. Besides, the GWK model is useful for revealing the endogenous and exogenous factors which affect the popularity of tweets.3) Motivated by the correlation between popularity and trendiness of topicsin Microblog and search trends, I have developed an economic analysis of the market involving a third-party ad broker, which is a popular market in current SEM, and finds that the adwords augmenting strategy with the trending and popular topics in Twitter enables the broker to achieve, on average, four folds larger return on investment than with a non-augmented strategy, while still maintaining the same level of risk
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Lagnier, Cédric. "Diffusion de l'information dans les réseaux sociaux." Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENM072/document.
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Prédire la diffusion de l'information dans les réseaux sociaux est une tâche difficile qui peut cependant permettre de répondre à des problèmes intéressants : recommandation d'information, choix des meilleurs points d'entrée pour une diffusion, etc. La plupart des modèles proposés récemment sont des extensions des modèles à cascades et de seuil. Dans ces modèles, le processus de diffusion est basé sur les interactions entre les utilisateurs du réseau (la pression sociale), et ignore des caractéristiques importantes comme le contenu de l'information diffusé ou le rôle actif/passif des utilisateurs. Nous proposons une nouvelle famille de modèles pour prédire la façon dont le contenu se diffuse dans un réseau en prenant en compte ces nouvelles caractéristiques : le contenu diffusé, le profil des utilisateurs et leur tendance à diffuser. Nous montrons comment combiner ces caractéristiques et proposons une modélisation probabiliste pour résoudre le problème de la diffusion. Ces modèles sont illustrés et comparés avec d'autres approches sur deux jeux de données de blogs. Les résultats obtenus sur ces jeux de données montrent que prendre en compte ces caractéristiques est important pour modéliser le processus de diffusion. Enfin, nous étudions le problème de maximisation de l'influence avec ces modèles et prouvons qu'il est NP-difficile, avant de proposer une adaptation d'un algorithme glouton pour approcher la solution optimalePredicting the diffusion of information in social networks is a key problem for applications like Opinion Leader Detection, Buzz Detection or Viral Marketing. Many recent diffusion models are direct extensions of the Cascade and Threshold models, initially proposed for epidemiology and social studies. In such models, the diffusion process is based on the dynamics of interactions between neighbor nodes in the network (the social pressure), and largely ignores important dimensions as the content diffused and the active/passive role users tend to have in social networks. We propose here a new family of models that aims at predicting how a content diffuses in a network by making use of additional dimensions : the content diffused, user's profile and willingness to diffuse. In particular, we show how to integrate these dimensions into simple feature functions, and propose a probabilistic modeling to account for the diffusion process. These models are then illustrated and compared with other approaches on two blog datasets. The experimental results obtained on these datasets show that taking into account these dimensions are important to accurately model the diffusion process. Lastly, we study the influence maximization problem with these models and prove that it is NP-hard, prior to propose an adaptation of the greedy algorithm to approximate the optimal solution
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Sundareisan, Shashidhar. "Making diffusion work for you: Classification sans text, finding culprits and filling missing values." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/49678.
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Can we find people infected with the flu virus even though they did not visit a doctor?
Can the temporal features of a trending hashtag or a keyword indicate which topic it belongs to without any textual information?
Given a history of interactions between blogs and news websites, can we predict blogs posts/news websites that are not in the sample but talk about the ``the state of the economy'' in 2008?
These questions have two things in common: a network (social networks or human contact networks) and a virus (meme, keyword or the flu virus) diffusing over the network. We can think of interactions like memes, hashtags, influenza infections, computer viruses etc., as viruses spreading in a network. This treatment allows for the usage of epidemiologically inspired models to study or model these interactions. Understanding the complex propagation dynamics involved in information diffusion with the help of these models uncovers various non-trivial and interesting results.
In this thesis we propose (a) A fast and efficient algorithm NetFill, which can be used to find quantitatively and qualitatively correct infected nodes, not in the sample and finding the culprits and (b) A method, SansText that can be used to find out which topic a keyword/hashtag belongs to just by looking at the popularity graph of the keyword without textual analysis.
The results derived in this thesis can be used in various areas like epidemiology, news and protest detection, viral marketing and it can also be used to reduce sampling errors in graphs.Master of Science
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Kim, Do Kyun. "Identifying Opinion Leaders by Using Social Network Analysis: A Synthesis of Opinion Leadership Data Collection Methods and Instruments." Ohio : Ohio University, 2007. http://www.ohiolink.edu/etd/view.cgi?ohiou1186672135.
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Bricker, Christine. "Vernacular geography and perceptions of place: a new approach to measuring American regional and political subcultures." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6374.
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This study develops a new theoretical approach and empirical measure of American regional subcultures using public opinion survey data and building on previous research (Chinni and Gimpel 2011; Elazar 1962, 1966; Hero 2000; Lieske 1993; Putnam, Leonardi and Nanetti 1994). Instead of approaching classification of regions based on formal geography, border states, population demography, ethnic groups and migration patterns, or historical traditions, this study uses a vernacular geography approach to study culture in the 50 American states. Vernacular geography is the sense of place revealed in ordinary people’s language. The study uses original nationwide survey data to measure perceptions of place based on states that are most similar to a respondent’s home state. The measure is based on unique survey questions where respondents have the freedom to choose any of the 50 states. The surveys are conducted by the Cooperative Congressional Election Study (CCES) from 2012 to 2016. These data allow development of a new measure of state similarity or regional subcultures based on vernacular geography. The state similarity network based on people’s feelings shows that state contiguity is not the driving factor in people’s perceptions of regions of the United States. It also shows that people’s perceptions of state similarity are a better predictor of policy diffusion than contiguity. Finally, this study shows that wealth is the most important factor in people’s perceptions of state similarity, but that population size, racial diversity, rural/urban population density, and ideology/partisanship are all predictors of people’s perceptions of state similarity at low levels. This study argues that perceptions of place matter. They are a core building block of political culture and are important for understanding policy diffusion. This study is about how citizens conceptualize their home state and network of most similar states, and whether state similarity networks, or social networks of states, influence government policy adoption and innovation.
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Kratzer, Jan, and Christopher Lettl. "Distinctive Roles of Lead Users and Opinion Leaders in the Social Networks of Schoolchildren." University of Chicago Press, 2009. http://dx.doi.org/10.1086/599324.
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Prior research has shown that both lead users and opinion leaders may propel the diffusion of innovation. This raises the question of whether lead users and opinion leaders are positioned similarly in social networks, which we address using a sample of 23 school classes consisting of 537 children. Research among children is very scarce in this particular domain. Our statistical analyses based on hierarchical linear modeling reveal two general results: first, lead users among children appear to possess a variety of links between clusters; second, opinion leaders are locally positioned within clusters of children and have many direct links. (authors' abstract)
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Engelbrecht, Adrian [Verfasser], Peter [Akademischer Betreuer] Buxmann, and Alexander [Akademischer Betreuer] Kock. "Discovery and Diffusion of Digital Innovations – An Analysis of Enterprise Social Networks and Data-Driven Business Models / Adrian Engelbrecht ; Peter Buxmann, Alexander Kock." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2019. http://d-nb.info/1177241692/34.
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Messarra, Nasri. "Stratégie du marketing viral sur Facebook : Analyses, expérimentations et études de cas sur les populations de diffusion initiales." Thesis, Montpellier, 2015. http://www.theses.fr/2015MONTD019/document.
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Le marketing sur les réseaux sociaux en ligne se caractérise par une perte de contrôle par rapport au marketing traditionnel (Mangold & Faulds, 2009) et ceci, pour deux raisons principales. La première étant que l'équilibre des forces est complètement inversé sur les réseaux sociaux en ligne au profit du consommateur. La deuxième étant que les plateformes, particulièrement Facebook, rendent l'accès aux informations propres à l'entreprise, comme par exemple la liste de ses fans, limité, compliqué, et dans certains cas impossible. Cette thèse s'inscrit à un moment où le besoin d'une base marketing stratégique pour l'utilisation des réseaux sociaux en ligne se fait sentir (Hoffman et Novak 2012). Pour cela, elle essaie de réfléchir et d'augmenter les recherches théoriques sur le marketing viral sur les réseaux sociaux en ligne et, en particulier la question de l'optimisation de la population initiale de diffusion. Elle essaie aussi de suggérer des stratégies permettant de rétablir un certain équilibre au profit du stratège marketing en lui montrant quels sont les moyens mis à sa disposition pour qu'il puisse jouir d'une certaine liberté de manœuvre entre une plateforme avare d'informations comme Facebook et un public qui considère les entreprises comme des envahisseurs sur son propre terrain (Fournier & Avery, 2011). Nous choisissons l'approche critique d'Habermas (1985). Dans ce paradigme, la connaissance est basée sur des expériences empiriques et la modification de l'environnement est acceptée comme un moyen d'atteindre la connaissance. Les analyses et expérimentations tentent de suivre un chemin théorique et expérimental pour aboutir à une réflexion théorique et pratique qui définirait des stratégies, des moyens et des méthodes d'optimisation du marketing viral sur Facebook à partir du contrôle des éléments du marketing viral et, principalement, la sélection ou la création de populations initiales de diffusion utilisables à cet effet. Le développement de cette thèse est composé de plusieurs chapitres rédigés en anglais, dont l'un est en troisième révision au Journal of Advertising Research (JAR) et dont quatre ont fait l'objet de communications à des conférences internationales (Euras, 2012; INSNA, 2014; ISMS, 2015) et au workshop stratégie Paris-Dauphine (2014). Dans cette thèse, nous avons posé plusieurs questions concernant le contrôle des éléments du marketing viral sur Facebook et, en particulier, l'optimisation des populations de diffusion initiale et nous pensons avoir trouvé des réponses innovantes et pertinentes à ces questions. Nous commençons par une réflexion générale sur la recherche sur les réseaux sociaux et, en particulier, l'éthique de la recherche sur Internet et les réseaux sociaux en ligne. Abordons ensuite la question de la reconstitution des graphes sociaux des fans, et analysons l'efficience et l'inefficacité des faux profils dans le cadre du marketing sur Facebook. Nous nous attaquons aussi au bouche-à-oreille négatif dans le cadre d'une population initiale de diffusion optimisée, et à la découverte de ponts surplombant les trous structurels entre deux camps politiques opposés comme population d'ensemencement théoriquement influente. Les expériences effectuées mènent sur une réflexion sur la question des supports disponibles sur Facebook (Pages, Groupes ou Profils) dans le cas des stratégies marketing en général et des stratégies de marketing viral en particulier. Le dernier chapitre met en application les résultats obtenus en prenant en charge une campagne de marketing viral du temps zéro jusqu'au succès. Cette expérience de Personal Branding, documentée et analysée, et sans biais puisqu'elle ne se passe que sur Facebook sans aucune publicité, montre concrètement l'efficacité de la stratégie de marketing viral définie à partir des résultats, réflexions et analyses théoriques de cette thèseOnline marketing is characterized by a loss of control when compared to traditional offline marketing (Mangold & Faulds, 2009). This is mainly due to two reasons. The first is that, on online social networks, communities took control over brands. The second is that online social network (OSN) platforms, mainly Facebook, limit access to information in a way they do not even have access to their list of fans.This dissertation comes at a time where more research and more investigations are needed in the field of strategic marketing on online social networks (Hoffman & Novak, 2012). This is why it attempts to add to theory and practice of viral marketing strategies on OSN and, in particular, initial seeding population. It is an attempt to reestablish equilibrium to the advantage of practitioners in marketing on OSN and increase their margin of manoeuver between a platform that builds barriers to information and a public who considers brands on OSN as intruders of its own space (Fournier & Avery, 2011). We chose the critical approach of Habermas (1985). In this paradigm, knowledge is based on empirical experiments where environment tweaking is allowed.The analysis and experiments in this dissertation follow a theoretical and experimental path that leads to a theoretical and practical thinking that defines strategies and viral marketing optimization methods through the control of viral marketing elements, especially the selection and creation of initial seeding populations.This dissertation consists of several chapters, amongst which a paper currently under third revision at the Journal of Advertising Research (JAR) and four experiments that have been presented at international conferences (Euras, 2012; INSNA, 2014; ISMS, 2015) and the workshop stratégie Paris-Dauphine (2014).This dissertation attempts to answer several questions about the control of viral marketing elements, especially the optimization of initial seeding population, and we think we have found relevant and innovation answers to these questions. We start with a meditation on research on online social networks and, in particular, the ethics of research on the internet and OSN. We then move to the question of uncovering and building social graphs of fans and analyze the efficiency and inefficiency of fake profiles in Facebook marketing. We also consider word-of-mouth in the context of an optimized seeding population, and the discovery of social bridges over structural holes rallying antagonistic political groups, a theoretically optimized seeding population. Those experiments lead to a thinking about the importance of the Facebook channel (pages, groups or personal timelines) and the role it plays with different marketing strategies: mass, relational, niche and, especially, viral.The last chapter puts into practice our findings and results by creating a strategy from zero to success of a personal branding campaign using viral marketing. This experiment is document, analyzed and without bias because it only happens on Facebook and does not use any advertising sources inside or outside this channel. This last experiment shows the efficiency of the viral marketing strategy based on our results, finding and theoretical analysis in this dissertation
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ALI, Arshad. "Topics in Delay Tolerant Networks (DTNs) : reliable transports, estimation and tracking." Phd thesis, Institut National des Télécommunications, 2012. http://tel.archives-ouvertes.fr/tel-00790766.
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Mobile Ad hoc NETworks (MANETs) aim at making communication between mobile nodes feasible without any infrastructure support. Sparse MANETs fall into the class of Delay Tolerant Networks which are intermittently connected networks and where there is no contemporaneous end-to-end path at any given time. We first, propose a new reliable transport scheme for DTNs based on the use of ACKnowledgments and random linear coding. We model the evolution of the network under our scheme using a fluid-limit approach. We optimize our scheme to obtain mean file transfer times on certain optimal parameters obtained through differential evolution approach. Secondly, we propose and study a novel and enhanced ACK to improve reliable transport for DTNs covering both unicast and multicast flows. We make use of random linear coding at relays so that packets can reach the destination faster. We obtain reliability based on the use of so-called Global Selective ACKnowledgment. We obtain significant improvement through G-SACKs and coding at relays. Finally, we tackle the problem of estimating file-spread in DTNs with direct delivery and epidemic routing. We estimate and track the degree of spread of a message in the network. We provide analytical basis to our estimation framework alongwith insights validated with simulations. We observe that the deterministic fluid model can indeed be a good predictor with a large of nodes. Moreover, we use Kalman filter and Minimum- Mean-Squared-Error (MMSE) to track the spreading process and find that Kalman filter provides more accurate results as compared to MMSE
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Kaufman, Dora. "Processo de tomada de decisão no ciberespaço: o papel das redes sociais no jogo das escolhas individuais." Pontifícia Universidade Católica de São Paulo, 2010. https://tede2.pucsp.br/handle/handle/5332.
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Dora Kaufman.pdf: 4902808 bytes, checksum: b1bed8be1d0495a11d1dbf87202fec71 (MD5)
Previous issue date: 2010-08-23The study presented below explores quality changes engendered by the internet in the decision-making process of individuals, showing "collective" subject and enunciators (brands, institutions, corporations, etc.) influence and interference. We address virtual communities role as a "Filter" of the information flow circulating on Internet, generating so-called "qualified information from fiduciary contracts established between the agents of such connections. These aspects will be examined through route studies offered and/or provided by the social network Facebook. The Facebook choice is random from an essence standpoint, but representative due to it now the largest virtual community and which, in Brazil, concentrates "Innovators" and Early Adopters , according to Everett Rogers definition. The study also be examines the interference of tools, developed from the databases of user registries and user selection choices on the Internet, and thus on Facebook, through which agents attempt to interfere and influence individual decisions. Using a theoretical base, one of the objectives of this study is to verify if the essence of Mark Granovetter s sociology theories of the eighties on the role of "Strong Ties" and "Weak Ties " in social networks, and Thomas Valente s diffusion of innovations process and "Behavior Contagion", is still valid and how they behave in the Cyberspace environment. The socio-economic drift of Foucault's analysis of "Human Capital" concept, which was later, expanded with "Social Capital", "Knowledge Society" and "Economy Creative " concepts
O estudo apresentado a seguir explora as mudanças qualitativas engendradas pelo advento da internet no processo de decisão dos indivíduos, mostrando as influências e interferências do sujeito coletivo e dos enunciadores (marcas, instituições, corporações, etc.). Abordamos o papel relevante das comunidades virtuais como filtro do fluxo de informações que circula na internet, gerando a chamada informação qualificada a partir da chancela de contratos de fidúcia firmados entre os agentes dessas conexões. Esses aspectos serão enfocados pela observação dos percursos propostos e/ou disponibilizados pela rede social Facebook. A escolha do Facebook é aleatória do ponto de vista de essência, mas representativa na medida em que é hoje a maior comunidade virtual do mundo, e no Brasil concentra os Innovators e os Early Adopters , segundo a definição de Everett Rogers. Serão também analisadas as interferências de ferramentas indutoras desenvolvidas especialmente a partir dos bancos de dados gerados pelos cadastros e movimentações dos usuários na própria Internet, e presente no Facebook, através das quais os agentes interessados tentam interferir e influenciar nas decisões dos indivíduos. Como base teórica, um dos propósitos é verificar se a essência das teses de sociologia dos anos 80 de Mark Granovetter sobre a função dos Laços Fortes e Laços Fracos nas redes sociais e de Thomas Valente sobre o processo de difusão de inovações e o comportamento de contágio ainda é válida e como elas se comportam no ambiente do Ciberespaço. O contexto socioeconômico deriva das análises de Foucault sobre o conceito de Capital Humano , que foi posteriormente expandido pelos conceitos de Capital Social , Sociedade do Conhecimento e Economia Criativa
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Van, der Pol Johannes. "Social network of firms, innovation and industrial performance." Thesis, Bordeaux, 2016. http://www.theses.fr/2016BORD0207/document.
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L’objectif de cette thèse est de répondre à trois questions principales ; commentexpliquer et interpréter un réseau de collaboration, est-ce que des firmes avec une positionparticulière dans un réseau bénéficient d’une performance accrue et enfin, existe-t-il desstructures de réseaux qui favorisent l’innovation ?Pour répondre à ces questions, la thèse est organisée en trois parties. La première partieprésente, dans un premier chapitre, une revue analytique de la littérature suivie d’un chapitrequi présente la théorie derrière une des méthodes d’analyse réseau utilisée dans cette thèse :les Exponential Random Graph Models (ERGM).La seconde partie présente trois analyses empiriques. Le premier chapitre empirique analysel’impact du cycle de vie de la technologie sur la dynamique du réseau de collaboration autourdes composites structuraux en aéronautique. Les deux chapitres suivants se concentrent sursecteur aéronautique et le secteur des biotechnologies respectivement. L’objectif de ceschapitres est d’analyser la dynamique structurelle et d’identifier s’il existe un lien entreposition dans le réseau et la performance de la firme.La dernière partie cherche à identifier des structures de réseaux qui favorisent l’innovation.Un modèle à base d’agents (ABM) est proposé pour répondre à cette questionThis thesis aims to answer three main questions ; how can one explain andinterpret the structure of an innovation network, are there positions in a network which allowfor an increased performance for firms and finally, are there network structures which favourinnovation ? In order to answer these questions, the thesis is organised in three parts.The first part presents, in a first chapter, an analytical review of the literature followed by achapter presenting the theory behind one of the network analysis methods: ExponentialRandom Graph Models (ERGM).The second part of the thesis presents three empirical analyses. The first empirical chapteranalyses the impact of the life-cycle of the technology on the structural dynamics of thecollaboration network for Structural Composite Materials. The following two chapters focuson two sectors, the aerospace and biotech sector. The aim of these chapters is to analyse thestructural dynamics of collaboration networks as well as identifying a link between networkposition and firm performance.The third and final part of this thesis searches for network structures which might favourinnovation. An Agent-Based Model is used to answer this final question
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Merrie, Andrew. "An idea whose time has come : an innovation perspective on Marine Spatial Planning." Thesis, Stockholms universitet, Stockholm Resilience Centre, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-65746.
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This study considers the emergence of Marine Spatial Planning as a tool for ecosystem-basedmanagement using an innovation diffusion perspective that combines elements of classical diffusionof innovations theory with a consideration of individual and networked agency and cross-scaledynamics in the context of a social-ecological systems perspective. The main findings are that thediffusion of Marine Spatial Planning does not follow a linear, technologically deterministic process asindicated by many diffusion of innovation studies but instead the diffusion process can becharacterised by a hybrid diffusion system that cascades over a series of phases and where individualsembedded in informal networks play a key role in driving the emergence of Marine Spatial Planningacross scales.This study provides a valuable contribution to considering how the study of innovation and diffusiondynamics could be applied to provide explanatory power when considering innovations that gobeyond being technological that might emerge in the context of addressing challenges facing linkedsocial-ecological systems at the global level and thus assist in resolving the mismatches between thescale of ecological processes and the governance of ecosystems to ensure an ongoing delivery ofecosystem services. These innovations are also important to study in the context of innovation being acatalyst for transformation of marine social-ecological systems to a more sustainable trajectory.
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Martinet, Lucie. "Réseaux dynamiques de terrain : caractérisation et propriétés de diffusion en milieu hospitalier." Thesis, Lyon, École normale supérieure, 2015. http://www.theses.fr/2015ENSL1010/document.
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Durant cette thèse, nous nous sommes intéressés aux outils permettant d'extraire les propriétés structurelles et temporelles de réseaux dynamiques ainsi que les caractéristiques de certains scénarios de diffusion pouvant s'opérer sur ces réseaux. Nous avons travaillé sur un jeu de données spécifiques, issu du projet MOSAR, qui comporte entre autre le réseau de proximité des personnes au cours du temps durant 6 mois à l'hôpital de Berk-sur-mer. Ce réseau est particulier dans le sens où il est constitué de trois dimensions: temporelle, structurelle par la répartition des personnes en services et fonctionnelle car chaque personne appartient à une catégorie socio-professionnelle. Pour chacune des dimensions, nous avons utilisé des outils existants en physique statistique ainsi qu'en théorie des graphes pour extraire des informations permettant de décrire certaines propriétés du réseau. Cela nous a permis de souligner le caractère très structuré de la répartition des contacts qui suit la répartition en services et mis en évidence les accointances entre certaines catégories professionnelles. Concernant la partie temporelle, nous avons mis en avant l'évolution périodique circadienne et hebdomadaire ainsi que les différences fondamentales entre l'évolution des interactions des patients et celle des personnels. Nous avons aussi présenté des outils permettant de comparer l'activité entre deux périodes données et de quantifier la similarité de ces périodes. Nous avons ensuite utilisé la technique de simulation pour extraire des propriétés de diffusion de ce réseau afin de donner quelques indices pour établir une politique de préventionIn this thesis, we focus on tools whose aim is to extract structural and temporal properties of dynamic networks as well as diffusion characteristics which can occur on these networks. We work on specific data, from the European MOSAR project, including the network of individuals proximity from time to time during 6 months at the Brek-sur-Mer Hospital. The studied network is notable because of its three dimensions constitution : the structural one induced by the distribution of individuals into distinct services, the functional dimension due to the partition of individual into groups of socio-professional categories and the temporal dimension.For each dimension, we used tools well known from the areas of statistical physics as well as graphs theory in order to extract information which enable to describe the network properties. These methods underline the specific structure of the contacts distribution which follows the individuals distribution into services. We also highlight strong links within specific socio-professional categories. Regarding the temporal part, we extract circadian and weekly patterns and quantify the similarities of these activities. We also notice distinct behaviour within patients and staff evolution. In addition, we present tools to compare the network activity within two given periods. To finish, we use simulations techniques to extract diffusion properties of the network to find some clues in order to establish a prevention policy
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Abdo, Alexandre Hannud. "Relações entre topologia e dinâmica em processos de crescimento e contágio em redes complexas." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/43/43134/tde-09032010-103716/.
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Esta tese apresenta um estudo de modelos de crescimento e contágio em redes, relacionando propriedades topológicas das redes com propriedades dinâmicas da evolução. Dos modelos de crescimento, desenvolve-se uma extensão do conceito de coeficiente de aglomeração para melhor determinar a validade desses modelos, demonstrando-se a utilidade de tal extensão em informar o papel não trivial da topologia dos ciclos entre modelos e redes reais. Dos modelos de contágio, utiliza-se uma modificação do modelo generalizado de Dodds e Watts para investigar o problema prático de planejar estratégias para difusão proposital de uma epidemia, em que se busca contaminar uma fração significativa de uma população a partir da inseminação de um grupo menor de indivíduos. Mostra-se que a modificação introduzida, a variabilidade da razão entre o tempo investido por influenciante e influenciável, determina o sentido e intensidade da relação entre o grau das sementes e o sucesso das estratégias. Esse problema também pode ser interpretado como um estudo de estabilidade com respeito a condições iniciais em que a variável em questão, o conjunto infectado, assume valores nos subconjuntos dos vértices do grafo.This thesis presents a study of models of network contagion and network growth, relating topological properties of the network with dynamical properties of the evolution. Of network growth models, an extension to the concept of clustering coefficient was applied to better determine the validity of some models, demonstrating the practical use of this extension and revealing the non-trivial role of the cycle topology between real-world networks and models. Of the network contagion models, an extension of Dodds-Watts generalized model of contagion was used to approach the practical problem of planning seeding strategies for large-scale interventions, where one wishes to infect some population through diffusion from a smaller initially infected group. It is shown that the extension introduced, which parametrizes the ratio between the time invested by the influential and the influenced, determines the direction and intensity of the relationship between the degree of the seeds and the success of strategies. This second problem can also be interpreted as a study of the stability with respect to initial conditions, where the relevant variable, the infected set, takes values on the subsets of the graphs vertices.
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Kurka, David Burth 1988. "Online social networks = knowledge extraction from information diffusion and analysis of spatio-temporal phenomena = Redes sociais online: extração de conhecimento e análise espaço-temporal de eventos de difusão de informação." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259074.
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Orientador: Fernando José Von ZubenDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
Made available in DSpace on 2018-08-27T03:14:35Z (GMT). No. of bitstreams: 1 Kurka_DavidBurth_M.pdf: 1660677 bytes, checksum: 7258daf8129b4dac9d1f647195775d3c (MD5) Previous issue date: 2015
Resumo: Com o surgimento e a popularização de Redes Sociais Online e de Serviços de Redes Sociais, pesquisadores da área de computação têm encontrado um campo fértil para o desenvolvimento de trabalhos com grande volume de dados, modelos envolvendo múltiplos agentes e dinâmicas espaço-temporais. Entretanto, mesmo com significativo elenco de pesquisas já publicadas no assunto, ainda existem aspectos das redes sociais cuja explicação é incipiente. Visando o aprofundamento do conhecimento da área, este trabalho investiga fenômenos de compartilhamento coletivo na rede, que caracterizam eventos de difusão de informação. A partir da observação de dados reais oriundos do serviço online Twitter, tais eventos são modelados, caracterizados e analisados. Com o uso de técnicas de aprendizado de máquina, são encontrados padrões nos processos espaço-temporais da rede, tornando possível a construção de classificadores de mensagens baseados em comportamento e a caracterização de comportamentos individuais, a partir de conexões sociais
Abstract: With the advent and popularization of Online Social Networks and Social Networking Services, computer science researchers have found fertile field for the development of studies using large volumes of data, multiple agents models and spatio-temporal dynamics. However, even with a significant amount of published research on the subject, there are still aspects of social networks whose explanation is incipient. In order to deepen the knowledge of the area, this work investigates phenomena of collective sharing on the network, characterizing information diffusion events. From the observation of real data obtained from the online service Twitter, we collect, model and characterize such events. Finally, using machine learning and computational data analysis, patterns are found on the network's spatio-temporal processes, making it possible to classify a message's topic from users behaviour and the characterization of individual behaviour, from social connections
Mestrado
Engenharia de Computação
Mestre em Engenharia Elétrica
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Parker, Jason Shaw. "Land tenure in the Sugar Creek watershed a contextual analysis of land tenure and social networks, intergenerational farm succession, and conservation use among farmers of Wayne County, Ohio /." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1147971583.
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Guha, Trupti. "Catching the video virus." Cleveland, Ohio : Cleveland State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=csu1210343957.
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Thesis (M.Ap.C.T. & M.)--Cleveland State University, 2008.Abstract. Title from PDF t.p. (viewed on July 11, 2008). Includes bibliographical references (p.111-118). Available online via the OhioLINK ETD Center. Also available in print.