Relevant bibliographies by topics / Endogenous diffusion social networks

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Academic literature on the topic 'Endogenous diffusion social networks'

Author: Grafiati
Published: 14 March 2023

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Journal articles on the topic "Endogenous diffusion social networks"

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Goldberg, Amir, and Sarah K. Stein. "Beyond Social Contagion: Associative Diffusion and the Emergence of Cultural Variation." American Sociological Review 83, no. 5 (2018): 897–932. http://dx.doi.org/10.1177/0003122418797576.

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Network models of diffusion predominantly think about cultural variation as a product of social contagion. But culture does not spread like a virus. We propose an alternative explanation we call associative diffusion. Drawing on two insights from research in cognition—that meaning inheres in cognitive associations between concepts, and that perceived associations constrain people’s actions—we introduce a model in which, rather than beliefs or behaviors, the things being transmitted between individuals are perceptions about what beliefs or behaviors are compatible with one another. Conventional contagion models require the assumption that networks are segregated to explain cultural variation. We show, in contrast, that the endogenous emergence of cultural differentiation can be entirely attributable to social cognition and does not require a segregated network or a preexisting division into groups. Moreover, we show that prevailing assumptions about the effects of network topology do not hold when diffusion is associative.
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Chang, Myong‐Hun, and Joseph E. Harrington, Jr. "Discovery and Diffusion of Knowledge in an Endogenous Social Network." American Journal of Sociology 110, no. 4 (2005): 937–76. http://dx.doi.org/10.1086/426555.

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Pavan, Elena. "Embedding Digital Communications Within Collective Action Networks: A Multidimensional Network Approach." Mobilization: An International Quarterly 19, no. 4 (2014): 441–55. http://dx.doi.org/10.17813/maiq.19.4.w24rl524u074126k.

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In this article, we conceive of digital media as embedded within social networks, and use this perspective to examine the role of online communications in collective action. We claim that the adoption of this perspective requires two shifts: first, rethinking the ontological separation between media and social networks of action that has, so far, characterized research in this domain; second, the adoption of flexible tools that enable us to account, simultaneously, for the multiplicity of relations underpinning collective efforts and the hybrid interplay between direct and technology-mediated interactions. After discussing the necessity and the implications of considering communication technologies as endogenous to social networks of collective action, we introduce multidimensional networks (MDNs) as a suitable perspective to advance the application of a relational approach to the study of collective action, thus meeting the challenges posed by the diffusion of interactive and networking digital media.
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Huang, Hung-Chun, and Hsin-Yu Shih. "Exploring the structure of international technology diffusion." Foresight 16, no. 3 (2014): 210–28. http://dx.doi.org/10.1108/fs-11-2012-0085.

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Purpose – This paper aims to provide a macro perspective on diffusion structure research, and to investigate the deep structure of international technology diffusion and structural differences between technology diffusion networks. This work also provides an understanding of the nature of globalization. Globalization has highlighted changes in socioeconomics and is reshaping the world. However, when comparing endogenous factors, exogenous factors are complex and demonstrate themselves as network phenomena. These network phenomena compose themselves as neither sole nor independent units. Countries in the global network act interdependently, and heavily influence one another. Design/methodology/approach – This study utilizes social network analysis to investigate the structural configuration of international technology diffusion. This investigation uses a sample of 42 countries over the period from 1997 to 2008. The data set contains two categories: bilateral trade flow and aggregate R&D expenditure. Meanwhile, this study uses block model analysis to reveal a network structure, which can precisely illustrate a global network configuration. Findings – The findings not only illustrate the pattern change of diffusion from a cascade-like to radial-like structure, but also present the structural configuration of technologically advanced countries and their competitive positions. Practical implications – In the shift to a diffusive structure, time and space are represented in new ways. Therefore, radial-like diffusion structure can provide some technological development approaches for countries interested in exogenous effects for technological growth and managing their international relation. Originality/value – This study is the first to use a multilateral perspective and longitudinal data to examine a cross-country network structure, to provide an understanding of the nature of globalization, its conceptualization and how influence and effects are transmitted through the interconnectedness of international technology diffusion.
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Koley, Paramita, Avirup Saha, Sourangshu Bhattacharya, Niloy Ganguly, and Abir De. "Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach." ACM Transactions on Knowledge Discovery from Data 15, no. 6 (2021): 1–25. http://dx.doi.org/10.1145/3449361.

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The networked opinion diffusion in online social networks is often governed by the two genres of opinions— endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news and feeds. Accurate demarcation of endogenous and exogenous messages offers an important cue to opinion modeling, thereby enhancing its predictive performance. In this article, we design a suite of unsupervised classification methods based on experimental design approaches, in which, we aim to select the subsets of events which minimize different measures of mean estimation error. In more detail, we first show that these subset selection tasks are NP-Hard. Then we show that the associated objective functions are weakly submodular, which allows us to cast efficient approximation algorithms with guarantees. Finally, we validate the efficacy of our proposal on various real-world datasets crawled from Twitter as well as diverse synthetic datasets. Our experiments range from validating prediction performance on unsanitized and sanitized events to checking the effect of selecting optimal subsets of various sizes. Through various experiments, we have found that our method offers a significant improvement in accuracy in terms of opinion forecasting, against several competitors.
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Wejnert, Barbara. "Diffusion, Development, and Democracy, 1800-1999." American Sociological Review 70, no. 1 (2005): 53–81. http://dx.doi.org/10.1177/000312240507000104.

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While a trend of growth in democratization over the past two centuries has been generally observed, it is the remarkable growth in the democratization of the world over the past 30 years that has truly captured the imagination of social scientists, policymakers, and the general public alike. Two major sets of factors have dominated studies attempting to predict democratization. One set characterizes endogenous or internal features of countries, and may be referred to as socioeconomic development. The other set, less often tested, characterizes exogenous variables that influence democratization via forces at work globally and within the region in which a country resides; this set may be referred to as diffusion processes. This study provides the first systematic comparison of these two sets of variables. When assessed alone, development indicators are robust predictors of democracy, but their predictive power fades with the inclusion of diffusion variables. In particular, diffusion predictors of spatial proximity and networks are robust predictors of democratic growth in both the world and across all regions. The results demonstrate that regional patterns in democratization are evident, and hence world analyses are only the first approximation to understanding democratic growth. Finally, this study introduces an application of Multilevel Regression Models to studies on democratization. Such models fit observed data on world democratization better than the simple regression models used in most previous studies.
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Ioannidis, Evangelos, Nikos Varsakelis, and Ioannis Antoniou. "Promoters versus Adversaries of Change: Agent-Based Modeling of Organizational Conflict in Co-Evolving Networks." Mathematics 8, no. 12 (2020): 2235. http://dx.doi.org/10.3390/math8122235.

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The social adoption of change is usually hard because in reality, forces opposing the social adoption of change manifest. This situation of organizational conflict corresponds to the case where two competing groups of influential agents (“promoters” versus “adversaries” of change) operate concurrently within the same organizational network. We model and explore the co-evolution of interpersonal ties and attitudes in the presence of conflict, taking into account explicitly the microscopic “agent-to-agent” interactions. In this perspective, we propose a new ties-attitudes co-evolution model where the diffusion of attitudes depends on the weights and the evolution of weights is formulated as a “learning mechanism” (weight updates depend on the previous values of both weights and attitudes). As a result, the co-evolution is intrinsic/endogenous. We simulate representative scenarios of conflict in 4 real organizational networks. In order to formulate structural balance in directed networks, we extended Heider’s definition of balance considering directed triangles. The evolution of balance involves two stages: first, negative links pop up disorderly and destroy balance, but after some time, as new negative links are formed, a “new” balance is re-established. This “new” balance is emerging concurrently with the polarization of attitudes or domination of one attitude. Moreover, same-minded agents are positively linked and different-minded agents are negatively-linked. This macroscopic self-organization of the system is due only to agent-to-agent interactions, involving feedbacks on weight updates at the local microscopic level.
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Xie, Xiaoyi, and Peiji Shi. "Dynamic Evolution and Collaborative Development Model of Urban Agglomeration in Hexi Corridor from the Perspective of Economic Flow." Land 12, no. 2 (2023): 274. http://dx.doi.org/10.3390/land12020274.

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Under the green goals of the carbon peak and carbon neutrality, understanding how to develop the economy with high quality is an important issue facing regional development. Based on the years 2000, 2010, and 2020, this paper studies the industrial function connection path and economic network characteristics of the Hexi Corridor through an urban flow model, dominant flow analysis, modified gravity model, and social network analysis method, and puts forward an economic synergistic development model. It is of great significance to strengthen the urban connection in the Hexi Corridor and give full play to the overall competitive advantage. The results are as follows. (1) The overall function of the urban agglomeration is weak, the outward function of manufacturing is outstanding, the complementary network is highly complicated and evolving, and the environment and public service and tourism industry have apparent advantages. (2) The backbone correlation axes of the “three industries” show the characteristics of a closed triangular connection, dual-core linkage development, and multi-center multi-axis interaction. (3) The economic network has a greater agglomeration effect than diffusion effect, with prominent grouping characteristics, forming a network structure of “one man, three vices, and many nodes” and a significant spatial proximity effect. (4) Based on geographical proximity, the “one axis, four circles, multiple points, and multiple channels” synergistic development model, which breaks administrative barriers, becomes the endogenous driving force for the evolution of the economic network.
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Hojman, Daniel A., and Adam Szeidl. "Endogenous networks, social games, and evolution." Games and Economic Behavior 55, no. 1 (2006): 112–30. http://dx.doi.org/10.1016/j.geb.2005.02.007.

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Christozov, Dimitar, and Stefka Toleva-Stoimenova. "Knowledge Diffusion via Social Networks." International Journal of Digital Literacy and Digital Competence 4, no. 2 (2013): 1–12. http://dx.doi.org/10.4018/jdldc.2013040101.

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The paper focuses on the phenomenon of social networks and their role in the process of knowledge diffusion. Social networks define the structure of a population of individuals. Diverse and dynamic environments lead to evolution of social networks as informing media. The Internet revolution affected especially the way people communicate and it naturally produced a new infrastructure for maintaining social networks. Different topologies of social networks are considered as different paths of knowledge diffusion. The paper addresses the challenges and opportunities this new infrastructure provides. It also argues for needs of “social network literacy” for successful and fruitful use of technology in solving the knowledge acquiring problem.
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Dissertations / Theses on the topic "Endogenous diffusion social networks"

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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.

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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.
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Bimpikis, Kostas. "Strategic delay and information exchange in endogenous social networks." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62405.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 160-165).<br>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.<br>by Kostas Bimpikis.<br>Ph.D.
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Pedersen, Tavis Joseph. "Tracking infection diffusion in social networks." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/62557.

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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.<br>Applied Science, Faculty of<br>Electrical and Computer Engineering, Department of<br>Graduate
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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.

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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.<br>published_or_final_version<br>Computer Science<br>Master<br>Master of Philosophy
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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.

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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.
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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.

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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.<br>published_or_final_version<br>Electrical and Electronic Engineering<br>Doctoral<br>Doctor of Philosophy
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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.

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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.<br>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.

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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 sociaux<br>Our 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&gt. "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.

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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.
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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.

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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.<br>Series: Department of Economics Working Paper Series
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Books on the topic "Endogenous diffusion social networks"

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Shakarian, Paulo, Abhivav Bhatnagar, Ashkan Aleali, Elham Shaabani, and Ruocheng Guo. Diffusion in Social Networks. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23105-1.

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Klochko, Marianna A. Endogenous time preferences in social networks. Edward Elgar Pub., 2005.

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1942-, Ordeshook Peter C., ed. Endogenous time preferences in social networks. Edward Elgar Pub., 2006.

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Acemoglu, Daron. Dynamics of information exchange in endogenous social networks. National Bureau of Economic Research, 2010.

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Acemoglu, Daron. Dynamics of information exchange in endogenous social networks. National Bureau of Economic Research, 2010.

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Windzio, Michael, Ivo Mossig, Fabian Besche-Truthe, and Helen Seitzer, eds. Networks and Geographies of Global Social Policy Diffusion. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83403-6.

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Assimilating identities: Social networks and the diffusion of sections. University of Sydney, 2005.

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1965-, Coutard Olivier, Hanley Richard, and Zimmerman Rae, eds. Sustaining urban networks: The social diffusion of large technical systems. Routledge, 2004.

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Social networks, innovation and the knowledge economy. Routledge, 2012.

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Wang, Haiyan, Feng Wang, and Kuai Xu. Modeling Information Diffusion in Online Social Networks with Partial Differential Equations. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38852-2.

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Book chapters on the topic "Endogenous diffusion social networks"

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Aggrawal, Niyati, and Adarsh Anand. "Information Diffusion." In Social Networks. CRC Press, 2022. http://dx.doi.org/10.1201/9781003088066-10.

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Duggan, Jim. "Diffusion Models." In Lecture Notes in Social Networks. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34043-2_5.

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Louni, Alireza, and K. P. Subbalakshmi. "Diffusion of Information in Social Networks." In Social Networking. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05164-2_1.

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Immorlica, Nicole. "Technology Diffusion in Social Networks." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-95891-8_5.

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Etesami, Seyed Rasoul. "Diffusion Games over Social Networks." In Potential-Based Analysis of Social, Communication, and Distributed Networks. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54289-8_7.

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Xu, Wen, Weili Wu, Lidan Fan, Zaixin Lu, and Ding-Zhu Du. "Influence Diffusion in Social Networks." In Optimization in Science and Engineering. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0808-0_27.

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Al-Taie, Mohammed Zuhair, and Seifedine Kadry. "Information Diffusion in Social Networks." In Advanced Information and Knowledge Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53004-8_8.

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Valente, Thomas W. "Social Networks, Diffusion Processes in." In Computational Complexity. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1800-9_181.

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Valente, Thomas W. "Social Networks, Diffusion Processes in." In Encyclopedia of Complexity and Systems Science. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_493.

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Bojic, Iva, Tomislav Lipic, and Vedran Podobnik. "Bio-inspired Clustering and Data Diffusion in Machine Social Networks." In Computational Social Networks. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4054-2_3.

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Conference papers on the topic "Endogenous diffusion social networks"

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De, Abir, Sourangshu Bhattacharya, and Niloy Ganguly. "Demarcating Endogenous and Exogenous Opinion Diffusion Process on Social Networks." In the 2018 World Wide Web Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3178876.3186121.

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Bimpikis, Kostas, Daron Acemoglu, and Asuman Ozdaglar. "Communication dynamics in endogenous social networks." In the Behavioral and Quantitative Game Theory. ACM Press, 2010. http://dx.doi.org/10.1145/1807406.1807499.

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Tatara, Eric, Nicholson Collier, Jonathan Ozik, and Charles Macal. "Endogenous Social Networks from Large-Scale Agent-Based Models." In 2017 IEEE International Parallel and Distributed Processing Symposium: Workshops (IPDPSW). IEEE, 2017. http://dx.doi.org/10.1109/ipdpsw.2017.83.

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Chanhyun Kang, C. Molinaro, S. Kraus, Y. Shavitt, and V. S. Subrahmanian. "Diffusion Centrality in Social Networks." In 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012). IEEE, 2012. http://dx.doi.org/10.1109/asonam.2012.95.

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Agrawal, Divyakant, Ceren Budak, and Amr El Abbadi. "Information diffusion in social networks." In the 20th ACM international conference. ACM Press, 2011. http://dx.doi.org/10.1145/2063576.2064036.

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Neves, Felipe, Victor Ströele, and Fernanda Campos. "Information Diffusion in Social Networks." In SBSI'19: XV Brazilian Symposium on Information Systems. ACM, 2019. http://dx.doi.org/10.1145/3330204.3330234.

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Mehdiabadi, Motahareh Eslami, Hamid R. Rabiee, and Mostafa Salehi. "Sampling from Diffusion Networks." In 2012 International Conference on Social Informatics (SocialInformatics). IEEE, 2012. http://dx.doi.org/10.1109/socialinformatics.2012.79.

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Balali, Ali, Aboozar Rajabi, Sepehr Ghassemi, Masoud Asadpour, and Hesham Faili. "Content diffusion prediction in social networks." In 2013 5th Conference on Information and Knowledge Technology (IKT). IEEE, 2013. http://dx.doi.org/10.1109/ikt.2013.6620114.

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Gayraud, Nathalie T. H., Evaggelia Pitoura, and Panayiotis Tsaparas. "Diffusion Maximization in Evolving Social Networks." In COSN'15: Conference on Online Social Networks. ACM, 2015. http://dx.doi.org/10.1145/2817946.2817965.

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Amato, Flora, Vincenzo Moscato, Antonio Picariello, and Giancarlo Sperlí. "Diffusion Algorithms in Multimedia Social Networks." In ASONAM '17: Advances in Social Networks Analysis and Mining 2017. ACM, 2017. http://dx.doi.org/10.1145/3110025.3116207.

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Reports on the topic "Endogenous diffusion social networks"

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Acemoglu, Daron, Kostas Bimpikis, and Asuman Ozdaglar. Dynamics of Information Exchange in Endogenous Social Networks. National Bureau of Economic Research, 2010. http://dx.doi.org/10.3386/w16410.

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Montgomery, Mark, and John Casterline. Social networks and the diffusion of fertility control. Population Council, 1998. http://dx.doi.org/10.31899/pgy6.1020.

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Hirshleifer, David, Lin Peng, and Qiguang Wang. News Diffusion in Social Networks and Stock Market Reactions. National Bureau of Economic Research, 2023. http://dx.doi.org/10.3386/w30860.

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Halberstam, Yosh, and Brian Knight. Homophily, Group Size, and the Diffusion of Political Information in Social Networks: Evidence from Twitter. National Bureau of Economic Research, 2014. http://dx.doi.org/10.3386/w20681.

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Rose, Erin M., and Beth A. Hawkins. Assessing the Potential of Social Networks as a Means for Information Diffusion the Weatherization Experiences (WE) Project. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1354643.

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Dillon, Andrew, Deanna Olney, Marie Ruel, et al. The diffusion of health knowledge through social networks: An impact evaluation of health knowledge asymmetries on child health in Burkina Faso. International Initiative for Impact Evaluation, 2014. http://dx.doi.org/10.23846/ow2170.

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Behrman, Jere R., Hans-Peter Kohler, and Susan Cotts Watkins. How can we measure the causal effects of social networks using observational data? Evidence from the diffusion of family planning and AIDS worries in South Nyanza District, Kenya. Max Planck Institute for Demographic Research, 2001. http://dx.doi.org/10.4054/mpidr-wp-2001-022.

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