To see the other types of publications on this topic, follow the link: Social network analysis.
Dissertations / Theses on the topic 'Social network analysis'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Social network analysis.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Junuthula, Ruthwik Reddy. "Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249.
Full text
2
CURZI, MIRCO. "Content based social network analysis." Doctoral thesis, Università Politecnica delle Marche, 2009. http://hdl.handle.net/11566/242305.
Full text
3
ATHANASIOU, THOMAS. "Multi-dimensional analysis of social multi-networks : Analysing a 5-layer social network case study." Thesis, Uppsala universitet, Institutionen för informatik och media, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-273908.
Full textAbstract:
Network theory analysis is applicable to many scientific disciplines (fields) such as biology, statistics and sociology. The social network analysis is one of the various branches of the broader network theory analysis, the social network analysis. It is of high interest among the researchers in social sciences. Social networks have had a significant impact on human civilizations for many centuries. During the last two decades, the main academic interest was addressed towards the research and analysis of a dynamically uprising sector of social networks, the on-line networks, primarily due to the domination of the Internet and technology over human attitudes and relations in modern societies. For many years, research was emphasized on the analysis of simple social networks, whilst during the last decade several researchers started working on the analysis of more complicated social networks, which consist by several smaller social networks. There are important differences between mono and multi-dimensional network analysis. Mono-dimensional analysis provides the research with relevant knowledge. On the other hand, multi-dimensional analysis is still at initial stage. As a result, several potential models related to the multi-networks analysis cannot always provide reliable and adequate outcomes. However, due to the fact that different social networks can be easily combined and form more extended and complicated networks, it is of high importance for the researchers to advance the multi-dimensional analysis and provide more adequate analytical models. The purpose of this thesis is to present the dynamic of the multi-dimensional analysis by consecutively applying both mono and multi-dimensional analysis on a social multi-network. The findings suggest that multi-dimensional analysis can add reliable knowledge on the social network analysis, but many problems that arose due the complexity of the multi-networks structures need to be addressed.
4
Vetro, Carla. "La social network analysis nella valutazione delle politiche sociali." Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/341.
Full textAbstract:
2009 - 2010Il tema della valutazione emerge periodicamente nella discussione politica italiana. L’azione del valutare, che rappresenta ormai un’operazione ricorrente nella vita quotidiana, diviene una pratica consolidata anche in seno alle istituzioni pubbliche, indispensabile per costruire un giudizio sul funzionamento delle politiche stesse. La pratica valutativa si rivela, però, difficile da applicare in contesti complessi e dinamici come quelli che caratterizzano gli interventi nel sociale, dove la complessità attiene alla eterogeneità e pluralità di attori coinvolti e alla multiproblematicità dei bisogni territoriali. Quando la riuscita di una politica di intervento dipende non solo dalle capacità di coordinamento dall’alto, cioè di chi programma gli interventi sociali e offre i servizi per rispondere ai bisogni di una comunità, ma anche dalla volontà e dalla partecipazione dal basso, cioè di chi fruisce degli interventi, risulta chiaro quanto un processo di valutazione diventi complesso. In tali situazioni, le tecniche della Social Network Analysis (di seguito analisi delle reti sociali) risultano particolarmente adatte a rilevare, studiare ed interpretare le interazioni di tutti gli attori coinvolti in uno o più interventi di politica sociale. Tali tecniche di analisi vengono utilizzate sempre più spesso nella ricerca valutativa, in quanto si presuppone che ci possa essere una relazione fra le caratteristiche della rete, costituita dagli attori sociali coinvolti nell’attuazione di un programma, e l’efficacia del programma stesso. [a cura dell'autore]
IX n.s.
5
Magnusson, Jonathan. "Social Network Analysis Utilizing Big Data Technology." Thesis, Uppsala universitet, Avdelningen för datalogi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-170926.
Full textAbstract:
As of late there has been an immense increase of data within modern society. This is evident within the field of telecommunications. The amount of mobile data is growing fast. For a telecommunication operator, this provides means of getting more information of specific subscribers. The applications of this are many, such as segmentation for marketing purposes or detection of churners, people about to switching operator. Thus the analysis and information extraction is of great value. An approach of this analysis is that of social network analysis. Utilizing such methods yields ways of finding the importance of each individual subscriber in the network. This thesis aims at investigating the usefulness of social network analysis in telecommunication networks. As these networks can be very large the methods used to study them must scale linearly when the network size increases. Thus, an integral part of the study is to determine which social network analysis algorithms that have this scalability. Moreover, comparisons of software solutions are performed to find product suitable for these specific tasks. Another important part of using social network analysis is to be able to interpret the results. This can be cumbersome without expert knowledge. For that reason, a complete process flow for finding influential subscribers in a telecommunication network has been developed. The flow uses input easily available to the telecommunication operator. In addition to using social network analysis, machine learning is employed to uncover what behavior is associated with influence and pinpointing subscribers behaving accordingly.
6
Hildorsson, Fredrik. "Scalable Solutions for Social Network Analysis." Thesis, Uppsala University, Department of Information Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-110548.
Full textAbstract:
A telecom operator can get a lot of high quality intelligence by studying the social network of its subscribers. One way to generate such a social network is to study the calls between the subscribers. Social networks generated from telecom networks can consist of millions of subscribers and the majority of the current social network analysis algorithms are too slow to analyze large networks. This master's thesis' objective is to find a more scalable solution to analyze social networks.
The work was divided into three steps; a survey of the existing solutions and algorithms, a pre-study to verify limitations of existing solutions and test some ideas and from the result of the pre-study and the survey a prototype was planned and implemented.
From the pre-study it was clear that the current solutions both took too long and used too much memory to be possible to use on a large social network. A number of algorithms were tested and from those a few was chosen to be implemented in the prototype. To help with the memory and time consumption the solution was also parallelized by using a partitioning algorithm to divide the graph into separate pieces on which each algorithm could run locally.The partitioning algorithm failed to scale well due to an internal modification of the partitioning scheme to adapt the partitioning to social graphs and simplify the parallelization. All but one algorithm scaled well and they were considerably faster than the original algorithms.
7
Grant, Eli. "Network analysis for social programme evaluation." Thesis, University of Oxford, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.719991.
Full text
8
FICARA, Annamaria. "Social network analysis approaches to study crime." Doctoral thesis, Università degli Studi di Palermo, 2022. http://hdl.handle.net/10447/537005.
Full textAbstract:
Social Network Analysis (SNA) studies groups of individuals and can be applied in a lot of areas such us organizational studies, psychology, economics, information science and criminology. One of the most important results of SNA has been the definition of a set of centrality measures (e.g., degree, closeness, betweenness, or clustering coefficient) which can be used to identify the most influential people with respect to their network of relationships.
The main problem with computing centrality metrics on social networks is the typical big size of the data. From the computational point of view, SNA represents social networks as graphs composed of a set of nodes connected by another set of edges on which the metrics of interest are computed.
To overcome the problem of big data, some computationally-light alternatives to the standard measures, such as Game of Thieves or WERW-Kpath, can be studied. In this regard, one of my main research activities was to analyze the correlation among standard and nonstandard centrality measures on network models and real-world networks.
The centrality metrics can greatly contribute to intelligence and criminal investigations allowing to identify, within a covert network, the most central members in terms of connections or information flow. Covert networks are terrorist or criminal networks which are built from the criminal relationships among members of criminal organizations. One of the most renowned criminal organizations is the Sicilian Mafia.
The focal point of my research work was the creation of two real-world criminal networks from the judicial documents of an anti-mafia operation called Montagna conducted by a specialized anti-mafia police unit of the Italian Carabinieri in Messina (i.e., the third largest city on the island of Sicily). One network includes meetings and the other one records telephone calls among suspected criminals of two Sicilian Mafia families. This dataset is unique and it might represent a valuable resource for better understanding complex criminal phenomena from a quantitative standpoint.
Different SNA approaches have been used on these Montagna networks to describe their structure and functioning, to predict missing links, to identify leaders or to evaluate police interventions aimed at dismantling and disrupting the networks. Graph distances have been used to find a network model able to properly mime the structure of a Mafia network and to quantify the impact of incomplete data not only on Mafia networks such as the Montagna ones but also on terrorist and street gangs networks.
The two simple Montagna networks have been finally used to build a multilayer network trying to obtain a more nuanced understanding of the network structure and of the strategic position of nodes in the network.
9
Moore, John David. "Making Sense of Networks: Exploring How Network Participants Understand and Use Information From Social Network Analysis." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/103640.
Full textAbstract:
Many of today's complex public issues are best addressed by multi-sectoral multi-organizational responses that include different types of organizations working together (Kettl, 2008; O'Toole, 1997). Social network analysis (SNA) of interorganizational networks has emerged as a useful tool for network managers to understand the structure and function of the complex networks in which they seek to manage (Human and Provan, 1997, 2000; Provan and Milward, 1995; Provan, Sebastian, and Milward, 1996; Provan, Veazie, Staten, and Teufel-Shone, 2005).
The output of an interorganizational SNA typically provides a range of information to network managers including network plots. The network plots provide visual representations of different aspects of the network by showing the kinds of ties between the actors in the network.
The information from network analyses can help network managers encourage systems thinking, see the different roles played by organizations, or identify links to outside resources among many other uses, but "will only have practical value to communities if it can be effectively presented, discussed, accepted, and acted on by community leaders and network participants [emphasis added]." (Provan et al., 2005, p. 610). However, little is currently known about if or how the information embedded in network plots is accepted or acted on by network participants.
The visual representations of the network (network plots) provided to network participants following a SNA are often open to a range of interpretations that may or may not align with the findings of the analyst or the intended use by network managers, raising many interesting questions. Little is currently known about how differently situated network participants might interpret the same network plots differently. Nor do we understand what factors might influence different individuals or organizations to come up with different interpretations.
After conducting a SNA and presenting it to network participants, I conducted interviews with a range of different representatives from participating organizations. I used a particular form of semi-structured interview, a situated micro-element interview from Dervin's Sense Making Methodology (SMM) (Dervin, Foreman-Wernet, and Lauterbach, 2003). I then analyzed the interview transcripts using standard qualitative coding methods (Bailey, 2007) to see if themes emerged that addressed the research questions.
I found that most informants had trouble extracting information and meaning from their examination of the plots without that meaning and interpretation being provided by the expert analyst. I posit some potential explanations for why that might be so in the case I studied. I then turn to some interesting methodological considerations that emerged from taking the perspectives of network participants seriously.
Finally, I synthesize the subject area and methodological findings into a refined framework for sense-making around network plots and offer propositions and potential approaches for future research.Doctor of Philosophy
Many of today's complex public issues are best addressed by multi-sectoral multi-organizational responses that include different types of organizations working together (Kettl, 2008; O'Toole, 1997). Social network analysis (SNA) of interorganizational networks has emerged as a useful tool for network managers to understand the structure and function of the complex networks in which they seek to manage (Human and Provan, 1997, 2000; Provan and Milward, 1995; Provan, Sebastian, and Milward, 1996; Provan, Veazie, Staten, and Teufel-Shone, 2005). The output of an interorganizational SNA typically provides a range of information to network managers including network plots. The network plots provide visual representations of different aspects of the network by showing the kinds of ties between the actors in the network. The information from network analyses can help network managers encourage systems thinking, see the different roles played by organizations, or identify links to outside resources among many other uses, but "will only have practical value to communities if it can be effectively presented, discussed, accepted, and acted on by community leaders and network participants [emphasis added]." (Provan et al., 2005, p. 610). However, little is currently known about if or how the information embedded in network plots is accepted or acted on by network participants. The visual representations of the network (network plots) provided to network participants following a SNA are often open to a range of interpretations that may or may not align with the findings of the analyst or the intended use by network managers, raising many interesting questions. Little is currently known about how differently situated network participants might interpret the same network plots differently. Nor do we understand what factors might influence different individuals or organizations to come up with different interpretations. After conducting a SNA and presenting it to network participants, I conducted interviews with a range of different representatives from participating organizations. I used a particular form of semi-structured interview, a situated micro-element interview from Dervin's Sense Making Methodology (SMM) (Dervin, Foreman-Wernet, and Lauterbach, 2003). I then analyzed the interview transcripts using standard qualitative coding methods (Bailey, 2007) to see if themes emerged that addressed the research questions. I found that most informants had trouble extracting information and meaning from their examination of the plots without that meaning and interpretation being provided by the expert analyst. I posit some potential explanations for why that might be so in the case I studied. I then turn to some interesting methodological considerations that emerged from taking the perspectives of network participants seriously. Finally, I synthesize the subject area and methodological findings into a refined framework for sense-making around network plots and offer propositions and potential approaches for future research.
10
Moore, John. "Making Sense of Networks: Exploring How Network Participants Understand and Use Information From Social Network Analysis." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103640.
Full textAbstract:
Many of today's complex public issues are best addressed by multi-sectoral multi-organizational responses that include different types of organizations working together (Kettl, 2008; O'Toole, 1997). Social network analysis (SNA) of interorganizational networks has emerged as a useful tool for network managers to understand the structure and function of the complex networks in which they seek to manage (Human and Provan, 1997, 2000; Provan and Milward, 1995; Provan, Sebastian, and Milward, 1996; Provan, Veazie, Staten, and Teufel-Shone, 2005).
The output of an interorganizational SNA typically provides a range of information to network managers including network plots. The network plots provide visual representations of different aspects of the network by showing the kinds of ties between the actors in the network.
The information from network analyses can help network managers encourage systems thinking, see the different roles played by organizations, or identify links to outside resources among many other uses, but "will only have practical value to communities if it can be effectively presented, discussed, accepted, and acted on by community leaders and network participants [emphasis added]." (Provan et al., 2005, p. 610). However, little is currently known about if or how the information embedded in network plots is accepted or acted on by network participants.
The visual representations of the network (network plots) provided to network participants following a SNA are often open to a range of interpretations that may or may not align with the findings of the analyst or the intended use by network managers, raising many interesting questions. Little is currently known about how differently situated network participants might interpret the same network plots differently. Nor do we understand what factors might influence different individuals or organizations to come up with different interpretations.
After conducting a SNA and presenting it to network participants, I conducted interviews with a range of different representatives from participating organizations. I used a particular form of semi-structured interview, a situated micro-element interview from Dervin's Sense Making Methodology (SMM) (Dervin, Foreman-Wernet, and Lauterbach, 2003). I then analyzed the interview transcripts using standard qualitative coding methods (Bailey, 2007) to see if themes emerged that addressed the research questions.
I found that most informants had trouble extracting information and meaning from their examination of the plots without that meaning and interpretation being provided by the expert analyst. I posit some potential explanations for why that might be so in the case I studied. I then turn to some interesting methodological considerations that emerged from taking the perspectives of network participants seriously.
Finally, I synthesize the subject area and methodological findings into a refined framework for sense-making around network plots and offer propositions and potential approaches for future research.Doctor of Philosophy
Many of today's complex public issues are best addressed by multi-sectoral multi-organizational responses that include different types of organizations working together (Kettl, 2008; O'Toole, 1997). Social network analysis (SNA) of interorganizational networks has emerged as a useful tool for network managers to understand the structure and function of the complex networks in which they seek to manage (Human and Provan, 1997, 2000; Provan and Milward, 1995; Provan, Sebastian, and Milward, 1996; Provan, Veazie, Staten, and Teufel-Shone, 2005). The output of an interorganizational SNA typically provides a range of information to network managers including network plots. The network plots provide visual representations of different aspects of the network by showing the kinds of ties between the actors in the network. The information from network analyses can help network managers encourage systems thinking, see the different roles played by organizations, or identify links to outside resources among many other uses, but "will only have practical value to communities if it can be effectively presented, discussed, accepted, and acted on by community leaders and network participants [emphasis added]." (Provan et al., 2005, p. 610). However, little is currently known about if or how the information embedded in network plots is accepted or acted on by network participants. The visual representations of the network (network plots) provided to network participants following a SNA are often open to a range of interpretations that may or may not align with the findings of the analyst or the intended use by network managers, raising many interesting questions. Little is currently known about how differently situated network participants might interpret the same network plots differently. Nor do we understand what factors might influence different individuals or organizations to come up with different interpretations. After conducting a SNA and presenting it to network participants, I conducted interviews with a range of different representatives from participating organizations. I used a particular form of semi-structured interview, a situated micro-element interview from Dervin's Sense Making Methodology (SMM) (Dervin, Foreman-Wernet, and Lauterbach, 2003). I then analyzed the interview transcripts using standard qualitative coding methods (Bailey, 2007) to see if themes emerged that addressed the research questions. I found that most informants had trouble extracting information and meaning from their examination of the plots without that meaning and interpretation being provided by the expert analyst. I posit some potential explanations for why that might be so in the case I studied. I then turn to some interesting methodological considerations that emerged from taking the perspectives of network participants seriously. Finally, I synthesize the subject area and methodological findings into a refined framework for sense-making around network plots and offer propositions and potential approaches for future research.
11
Rajasekaran, Sathya Dev Squicciarini Anna C. Metzner John J. "Social network risk analysis and privacy framework." [University Park, Pa.] : Pennsylvania State University, 2009. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-4812/index.html.
Full text
12
Afrasiabi, Rad Amir. "Social Network Analysis and Time Varying Graphs." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34441.
Full textAbstract:
The thesis focuses on the social web and on the analysis of social networks with particular emphasis on their temporal aspects. Social networks are represented here by Time Varying Graphs (TVG), a general model for dynamic graphs borrowed from distributed computing.
In the first part of the thesis we focus on the temporal aspects of social networks. We develop various temporal centrality measures for TVGs including betweenness, closeness, and eigenvector centralities, which are well known in the context of static graphs. Unfortunately the computational complexities of these temporal centrality metrics are not comparable with their static counterparts. For example, the computation of betweenness becomes intractable in the dynamic setting. For this reason, approximation techniques will also be considered. We apply these temporal measures to two very different datasets, one in the context of knowledge mobilization in a small community of university researchers, the other in the context of Facebook commenting activities among a large number of web users. In both settings, we perform a temporal analysis so to understand the importance of the temporal factors in the dynamics of those networks and to detect nodes that act as “accelerators”.
In the second part of the thesis, we focus on a more standard static graph representation. We conduct a propagation study on YouTube datasets to understand and compare the propagation dynamics of two different types of users: subscribers and friends. Finally, we conclude the thesis with the proposal of a general framework to present, in a comprehensive model, the influence of the social web on e-commerce decision making.
13
Rezaee, Shaliz. "E-mail Prioritization through Social Network Analysis." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3356.
Full textAbstract:
Context. Trust and reliability are important issues in online communication. By rapid growth of online social networks (OSNs), online communication becomes richer by the integrating of social interaction into the communication model. However, E-mail communication systems concern about unsolicited messages. Objectives. In this thesis the aim is to investigate how to prioritize E-mails between recipients and senders by using information from OSNs. Methods. An algorithm is presented for computing trust by measuring users‟ interaction and similarity in online social networks and this trust is used by another algorithm for prioritizing the E-mail inbox. Results. An evaluation of the proposed method is performed via a case study and the prediction error of the method is compared with the prediction error of the random feedback. The error of the method is significantly lower than random feedback and is relatively low, given the small number of observations. Conclusions. This thesis contributes in its review and categorization of existing trust models. Furthermore, it provides an analysis on how to use social information for E-mail prioritization. Based on the analysis, a method is presented for improving the reliability of E-mail communication by extracting information from OSNs. The information is used for computing the trust score between two OSN friends. In this thesis, it is suggested that, inbox prioritization is achievable using the selected method.This thesis has addressed E-mail prioritization through social network by using social information. The task has been done by focusing on the interaction and similarity between friends in the OSN. A theoretical analysis has been performed in order to identify the characteristic of suitable trust model. An algorithm (Algorithm 1) has been suggested to estimate weights of different criteria of social information. In order to have the trust predictions based on the user‟s preferences, the algorithm adjusted the weights based on the user‟s feedback. In addition, another algorithm (Algorithm 2) has been proposed to compute trust scores and prioritize E-mails inbox. Finally, an algorithm (Algorithm 3) has been presented to evaluate the error of the computed (predicted) trust scores. In order to display the applicability of the method as well as to motivate the theoretical foundation, a case study was reported in which the proposed method was applied to Facebook. The analysis showed that the proposed method was feasible to be used, and it provided users a mean to prioritize E-mail inboxes based on the social information extracted from Facebook. The analysis indicated that least squares method was a suitable approach to estimate weights that were used in computing trust scores and thus prioritizing E-mails inbox.
14
Wang, Xin. "Graph pattern matching on social network analysis." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8277.
Full textAbstract:
Graph pattern matching is fundamental to social network analysis. Its effectiveness for identifying social communities and social positions, making recommendations and so on has been repeatedly demonstrated. However, the social network analysis raises new challenges to graph pattern matching. As real-life social graphs are typically large, it is often prohibitively expensive to conduct graph pattern matching over such large graphs, e.g., NP-complete for subgraph isomorphism, cubic time for bounded simulation, and quadratic time for simulation. These hinder the applicability of graph pattern matching on social network analysis. In response to these challenges, the thesis presents a series of effective techniques for querying large, dynamic, and distributively stored social networks. First of all, we propose a notion of query preserving graph compression, to compress large social graphs relative to a class Q of queries. We then develop both batch and incremental compression strategies for two commonly used pattern queries. Via both theoretical analysis and experimental studies, we show that (1) using compressed graphs Gr benefits graph pattern matching dramatically; and (2) the computation of Gr as well as its maintenance can be processed efficiently. Secondly, we investigate the distributed graph pattern matching problem, and explore parallel computation for graph pattern matching. We show that our techniques possess following performance guarantees: (1) each site is visited only once; (2) the total network traffic is independent of the size of G; and (3) the response time is decided by the size of largest fragment of G rather than the size of entire G. Furthermore, we show how these distributed algorithms can be implemented in the MapReduce framework. Thirdly, we study the problem of answering graph pattern matching using views since view based techniques have proven an effective technique for speeding up query evaluation. We propose a notion of pattern containment to characterise graph pattern matching using views, and introduce efficient algorithms to answer graph pattern matching using views. Moreover, we identify three problems related to graph pattern containment, and provide efficient algorithms for containment checking (approximation when the problem is intractable). Fourthly, we revise graph pattern matching by supporting a designated output node, which we treat as “query focus”. We then introduce algorithms for computing the top-k relevant matches w.r.t. the output node for both acyclic and cyclic pattern graphs, respectively, with early termination property. Furthermore, we investigate the diversified top-k matching problem, and develop an approximation algorithm with performance guarantee and a heuristic algorithm with early termination property. Finally, we introduce an expert search system, called ExpFinder, for large and dynamic social networks. ExpFinder identifies top-k experts in social networks by graph pattern matching, and copes with the sheer size of real-life social networks by integrating incremental graph pattern matching, query preserving compression and top-k matching computation. In particular, we also introduce bounded (resp. unbounded) incremental algorithms to maintain the weighted landmark vectors which are used for incremental maintenance for cached results.
15
Bohn, Angela, Norbert Walchhofer, Patrick Mair, and Kurt Hornik. "Social Network Analysis of Weighted Telecommunications Graphs." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2009. http://epub.wu.ac.at/708/1/document.pdf.
Full textAbstract:
SNA provides a wide range of tools that allow examination of telecommunications graphs. Those graphs contain vertices representing cell phone users and lines standing for established connections. Many sna tools do not incorporate the intensity of interaction. This may lead to wrong conclusions because the difference between best friends and random contacts can be defined by the accumulated duration of talks. To solve this problem, we propose a closeness centrality measure (ewc) that incorporates line values and compare it to Freeman's closeness. Small exemplary networks will demonstrate the characteristics of the weighted closeness compared to other centrality measures. Finally, the ewc will be tested on a real-world telecommunications graph provided by a large Austrian mobile service provider and the advantages of the ewc will be discussed.Series: Research Report Series / Department of Statistics and Mathematics
16
Nohuddin, Puteri. "Predictive trend mining for social network analysis." Thesis, University of Liverpool, 2012. http://livrepository.liverpool.ac.uk/7153/.
Full textAbstract:
This thesis describes research work within the theme of trend mining as applied to social network data. Trend mining is a type of temporal data mining that provides observation into how information changes over time. In the context of the work described in this thesis the focus is on how information contained in social networks changes with time. The work described proposes a number of data mining based techniques directed at mechanisms to not only detect change, but also support the analysis of change, with respect to social network data. To this end a trend mining framework is proposed to act as a vehicle for evaluating the ideas presented in this thesis. The framework is called the Predictive Trend Mining Framework (PTMF). It is designed to support "end-to-end" social network trend mining and analysis. The work described in this thesis is divided into two elements: Frequent Pattern Trend Analysis (FPTA) and Prediction Modeling (PM). For evaluation purposes three social network datasets have been considered: Great Britain Cattle Movement, Deeside Insurance and Malaysian Armed Forces Logistic Cargo. The evaluation indicates that a sound mechanism for identifying and analysing trends, and for using this trend knowledge for prediction purposes, has been established.
17
Hu, Daning. "Analysis and Applications of Social Network Formation." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/145710.
Full textAbstract:
Nowadays people and organizations are more and more interconnected in the forms of social networks: the nodes are social entities and the links are various relationships among them. The social network theory and the methods of social network analysis (SNA) are being increasingly used to study such real-world networks in order to support knowledge management and decision making in organizations. However, most existing social network studies focus on the static topologies of networks. The dynamic network link formation process is largely ignored. This dissertation is devoted to study such dynamic network formation process to support knowledge management and decision making in networked environments. Three challenges remain to be addressed in modeling and analyzing the dynamic network link formation processes. The first challenge is about modeling the network topological changes using longitudinal network data. The second challenge is concerned with examining factors that influence formation of links among individuals in networks. The third challenge is regarding link prediction in evolving social networks. This dissertation presents four essays that address these challenges in various knowledge management domains. The first essay studies the topological changes of a major international terrorist network over a 14-year period. In addition, this paper used a simulation approach to examine this network's vulnerability to random failures, targeted attacks, and real world authorities' counterattacks. The second essay and third essay focuses on examining determinants that significantly influence the link formation processes in social networks. The second essay found that mutual acquaintance and vehicle affiliations facilitate future co-offending link formation in a real-world criminal network. The third essay found that homophily in programming language preference, and mutual are determinants for forming participation links in an online Open Source social network. The fourth essay focuses on the link prediction in evolving social networks. It proposes a novel infrastructure for describing and utilizing the discovered determinants of link formation process (i.e. semantics of social networks) in link prediction to support expert recommendation application in an Open Source developer community. It is found that the integrated mechanism outperforms either user-based or Top-N most recognized mechanism.
18
MACCAGNOLA, DANIELE. "Relational Learning Models for Social Network Analysis." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2016. http://hdl.handle.net/10281/100459.
Full textAbstract:
Social networks have been studied for nearly half a century by sociologists to analyze interactions between people. Nowadays, with the advent of Web 2.0, social networks have moved from being an abstract concept to actual online applications such as Facebook, Twitter and Linkedin, which are used daily by people to create and maintain relationships with friends, co-workers and other acquaintances.
However, online social networks allow their users to do more than just maintain friendships: people can generally create and share content in various forms, from simple textual messages (called posts) to photos, videos, audios and much more.
Several approaches in Social Network Analysis have been proposed in the years to extract knowledge from social networks, addressing tasks that ranges from understanding how users create and modify their relationships, to finding the most influential people in a group, to understanding the ideas and opinions expressed by people in their posts.
Many techniques from the field of Machine Learning have been used to address these problems. While some of them exploit the relationships among users, others focus on the content generated by the users, typically by analyzing the textual content written in the posts. These approaches, however, are generally unable to exploit both, in this way ignoring a consistent part of information available in social networks.
The field of Relational Learning tries to overcome this limitation, by extending traditional approaches in order to use both sources of information, and thus achieve better performances.
In this thesis, I propose new Relational Learning approaches that address two tasks in Social Network Analysis. The first task is Community Discovery, which objective is to detect groups of users that share strong connections (e.g. working in the same company, attended the same school, etc.) or sharing the same interests. While this task is generally addressed by considering only the network structure, adding the user content can allow to increase the performance.
The second task is Opinion Detection, which objective is to infer the opinion of users about a specific topic (politics, likeness of a brand). This task is typically addressed using user textual content, but the relationships can provide additional insights that allow to improve the inference of users' opinions.
The experimental investigations reveal that network structure and user-generated content provide complementary information, and that using both sources of data can improve the performance of algorithms in both community discovery (a structure-based task) and opinion detection (a content-based task).
19
Kibanov, Mark [Verfasser]. "Social Network Mining for Analysis of Social Phenomena / Mark Kibanov." Kassel : Universitätsbibliothek Kassel, 2019. http://d-nb.info/1193090261/34.
Full text
20
Lee, Changheon. "Dynamics of Advice Network and Knowledge Contribution: A Longitudinal Social Network Analysis." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/243117.
Full textAbstract:
Online communities have become an increasingly popular channel for social interaction, enabling knowledge and opinion sharing across a board range of topics and contexts. Their viability and sustainability depends largely on contributions from community members in terms of time, resources, and knowledge. However, how individuals' knowledge contribution behavior changes over time and what network structural characteristics influence individuals' contribution behavior is not well understood. This study investigates "co-evolution" of social networks (i.e. advice network) and knowledge contribution behavior thorough a lens of social selection and social influence mechanism. This study are particularly interested in examining the dynamics of the advice network ties and the knowledge contribution behavior in the context of virtual financial communities in which people voluntarily participate to exchanges investing-related information. Unlike popular friendship-based online social networks, virtual financial communities in this study enables members to construct their own advice network by adding, maintaining, or terminating advice ties. Changes in network ties are referred to as social selection, while changes in individuals' behavior in response to the current network position are referred to as social influence. Dynamic network modeling is applied to investigate effects of social selection and influence separately and then examine the interplay between social selection and behavioral influence. Examination of such effects both separately and simultaneously requires a longitudinal data that capture dynamic changes in both the advice ties and the behavior under study.
21
Giles-Summers, Brandon. "Targeting Social Network Analysis in Counter IED Operations." Thesis, Monterey, California. Naval Postgraduate School, 2011. http://hdl.handle.net/10945/5703.
Full textAbstract:
Approved for public release; distribution is unlimited.The purpose of this research is to provide insights to Commanders in the field for attack-the-network (AtN) operations in the fight against Improved Explosive Devices (IED). Established in 2006, the Improved Explosive Devices Defeat Organization (JIEDDO) has spent billions of dollars to execute its operational mandate: defeat the device, attack the network, and train the force. JIEDDO has excelled in training the force and defeating the device, but lagged behind in providing necessary information to facilitate attack-the-network operations. To facilitate AtN operations, JIEDDO created a Counter-IED Operation Integration Center (COIC), which provides analysis, but utilizes metrics that are not necessarily intuitive. Rather than metrics, what commanders need is a clear understanding of what attack the network means in order to create lines of operations that undermine networks that use IEDs. The goal of this thesis, therefore, is to define attack-the-network, introduce social network analysis, provide a focused discussion on how to apply social relational information to operations, determine a targeted person's relevance, provide operational commanders with a basic matrix to gain perspective on social interactions of network members, and offer case studies illuminating the difficulties inherent in network targeting.
22
Abbas, Syed Muhammad Ali. "Design and analysis of social network systems (SNS)." Thesis, Manchester Metropolitan University, 2016. http://e-space.mmu.ac.uk/619490/.
Full textAbstract:
In the last few years, online Social Network Systems (SNSs) thrived and changed the overall outlook of the Internet. These systems play an important role in making the Internet social, a hallmark of Web 2.0. Various such systems have been developed to serve a diverse set of needs. SNSs provide not only a space for self-representation, but also mechanisms to build and maintain one’s social network online. A lot of studies have been carried out on such systems to identify how people develop cultures of communication, sharing and participation and also to identify the network structure of such systems. In this thesis, we carry this line of research forward. Our aim is the identification of some key user characteristics and social processes which result in the emergence of a social network. These might help future platform and application developers in creating better, more efficient and more open and user-friendly SNSs. Specifically, we make the following three major contributions: a) One of the distinct features of an SNS is the public listing of friendship links - social network. Most of the personal details such as hometown and workplace information have been hidden from non-friends, but the list of friendships remains open. Being a true representation, people use their real names as their screen names. Such names alone contain detailed cultural information about their ethnicities, religion and even their geographical origins. Our first contribution is that we have made good use of such information by inferring ethnic classification of users of Facebook. We identified how clustered and segregated the overall social network is when users’ inferred ethnicity is taken into account. Different cultures have different behaviours with distinct characteristics. This rich information can be used to develop an understanding and help create diverse applications catering for specific ethnicities and geographical regions; covering both the dominant and non-dominant groups. We have identified ethnicities of a subset of Facebook users with their friends and studied how different ethnicities are connected among and within each other. A large social network dataset of four thousand Manchester Metropolitan University (MMU) students have been selected from Facebook. We have extensively analysed this dataset for its network structure and also its semantic and social structure. Our work suggests our dataset is clustered and segregated on ethnic lines. b) To develop a user liberating SNS where the control and the ownership of rich personal data is in the hands of SNS users, a clear understanding is required of how such systems on an individual and group level are developed and maintained. Never before in Social Sciences was it possible to study society on such a large scale. These systems have facilitated the study of individuals both at a local and global scale. However, at the moment very little knowledge is available to identify how people develop their friendship in reality. So for example, it is not known whether in SNSs people meet others based on their attributes and interests, or if they simply bring online their real lives’ social networks. And more specifically, what processes does one go through to develop her social network. To fill this knowledge gap in this thesis, as our second contribution, we have used a computer simulation technique known as Agent-Based simulation, to develop four simulation models based on both individuals’ affinities and environmental aspects. Specifically, we have developed models of student interaction to develop social networks. Three University’s datasets which include Caltech (Nodes 762, Edges 16651), Princeton (Nodes 6575, Edges 293307) and Georgetown (Nodes 9388, Edges 425619), have been used to check the performance and rigour of the model. Our evidence suggests that ‘friend-of-a-friend’ (FOAF) best represents social interactions in Caltech University. In the case of Princeton and Georgetown, we found a multitude of social and structural processes involved, which are: attribute based (same dormitory, major or high school etc.), social interaction, random meet ups (through parties or other social events) and current friends introducing new friends. c) We observe that in the main, SNSs are centralised, and depend solely on central entities for everything. With huge personal data on such SNSs, advertising and marketing agencies have made very sophisticated systems to gather information about people. It is a goldmine for them for personalised advertisement. Also various governmental agencies have been using SNSs as an excuse to curb potential threats both legally and illegally, to obtain information on numerous users (people). In order to deal with such issues inherent in centralised client-server architecture, as the third contribution of this thesis, we have proposed and implemented a completely decentralised SNS in a peer-to-peer fashion. Our implementation is done in an open source Peer-To-Peer (P2P) client Tribler. To handle the dynamicity of users in a P2P system – their availability, we have developed mechanisms to deal with it. This SNS has been evaluated on a deployed system with real users. This prototype establishes the feasibility of a totally distributed SNS, but its practicality when scaled to a full system would require more work.
23
Bohn, Angela, Ingo Feinerer, Kurt Hornik, and Patrick Mair. "Content-Based Social Network Analysis of Mailing Lists." The R Foundation for Statistical Computing, 2011. http://epub.wu.ac.at/5435/1/RJournal_2011%2D1_Bohn~et~al.pdf.
Full textAbstract:
Social Network Analysis (SNA) provides
tools to examine relationships between
people. Text Mining (TM) allows capturing the
text they produce inWeb 2.0 applications, for example,
however it neglects their social structure.
This paper applies an approach to combine the
two methods named "content-based SNA". Using
the R mailing lists, R-help and R-devel, we
show how this combination can be used to describe
people's interests and to find out if authors
who have similar interests actually communicate.
We find that the expected positive
relationship between sharing interests and communicating
gets stronger as the centrality scores
of authors in the communication networks increase.
24
Pan, Long. "Effective and Efficient Methodologies for Social Network Analysis." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/25962.
Full textAbstract:
Performing social network analysis (SNA) requires a set of powerful techniques to analyze structural information contained in interactions between social entities. Many SNA technologies and methodologies have been developed and have successfully provided significant insights for small-scale interactions. However, these techniques are not suitable for analyzing large social networks, which are very popular and important in various fields and have special structural properties that cannot be obtained from small networks or their analyses. There are a number of issues that need to be further studied in the design of current SNA techniques. A number of key issues can be embodied in three fundamental and critical challenges: long processing time, large computational resource requirements, and network dynamism.
In order to address these challenges, we discuss an anytime-anywhere methodology based on a parallel/distributed computational framework to effectively and efficiently analyze large and dynamic social networks. In our methodology, large social networks are decomposed into intra-related smaller parts. A coarse-level of network analysis is built based on comprehensively analyzing each part. The partial analysis results are incrementally refined over time. Also, during the analyses process, network dynamic changes are effectively and efficiently adapted based on the obtained results. In order to evaluate and validate our methodology, we implement our methodology for a set of SNA metrics which are significant for SNA applications and cover a wide range of difficulties. Through rigorous theoretical and experimental analyses, we demonstrate that our anytime-anywhere methodology isPh. D.
25
Zhao, Meng John. "Analysis and Evaluation of Social Network Anomaly Detection." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/79849.
Full textAbstract:
As social networks become more prevalent, there is significant interest in studying these network data, the focus often being on detecting anomalous events. This area of research is referred to as social network surveillance or social network change detection. While there are a variety of proposed methods suitable for different monitoring situations, two important issues have yet to be completely addressed in network surveillance literature. First, performance assessments using simulated data to evaluate the statistical performance of a particular method. Second, the study of aggregated data in social network surveillance. The research presented tackle these issues in two parts, evaluation of a popular anomaly detection method and investigation of the effects of different aggregation levels on network anomaly detection.Ph. D.
26
Yu, En. "Social Network Analysis Applied to Ontology 3D Visualization." Miami University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=miami1206497854.
Full text
27
Caulfield, John. "A social network analysis of Irish language use in social media." Thesis, Cardiff University, 2013. http://orca.cf.ac.uk/53228/.
Full textAbstract:
Statistics show that the world wide web is dominated by a few widely spoken languages. However, in quieter corners of the web, clusters of minority language speakers can be found interacting and sharing content. This study is the first to compare three such clusters of Irish language social media users. Social network analysis of the most active public sites of interaction through Irish – the Irish language blogosphere, the Irish language Twittersphere and a popular Irish language Facebook group – reveals unique networks of individuals communicating through Irish in unique and innovative ways. Firstly, it describes the members and their activity, and the size and structure of the networks they share. Then through focused discourse analysis of the core prolific users in each network it describes how the language has been adapted to computer-mediated communication. This study found that the largest networks of Irish speakers comprised between 150-300 regular participants each. Most members were adults, male, and lived in towns and cities outside of the language’s traditional heartland. Moreover, each group shared one common trait: though scattered geographically, through regular online interaction between core members they behave like communities. They were found to have shared histories, norms and customs, and self-awareness that their groups were unique. Furthermore, core users had adapted the language in new and innovative ways through their online discourse. This study is the first comprehensive audit of who is using the Irish language socially on the web, where they are forming networks online, and how they are adapting the language to online discourse. It makes a unique contribution in re-imagining what constitutes an Irish language community in the context of the Network Society. In the process, it contributes to the growing body of sociolinguistic research into globalisation and local identity on the web.
28
Fidalgo, Patrícia Seferlis Pereira. "Learning networks and moodle use in online courses: a social network analysis study." Doctoral thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8862.
Full textAbstract:
Dissertação para obtenção do Grau de Doutor em Ciências da Educação Especialidade em Tecnologias, Redes e Multimédia na Educação e FormaçãoThis research presents a case study on the interactions between the participants of the forums of four online undergraduate courses from the perspective of social network analysis (SNA). Due to lack of studies on social networks in online learning environments in higher education in Portugal we have choose a qualitative structural analysis to address this phenomenon. The context of this work was given by the new experiences in distance education (DE) that many institutions have been making. Those experiences are a function of the changes in educational paradigms and due to a wider adoption of Information and Communication Technologies (ICT) from schools as well as to the competitive market. Among the technologies adopted by universities are the Learning Management Systems (LMSs) that allow recording, storing and using large amounts of relational data about their users and that can be accessed through Webtracking. We have used this information to construct matrices that allowed the SNA. In order to deepen knowledge about the four online courses we were studying we have also collect data with questionnaires and interviews and we did a content analysis to the participations in the forums. The three main sources of data collection led us to three types of analysis: SNA, statistical analysis and content analysis. These types of analysis allowed, in turn, a three-dimensional study on the use of the LMS: 1) the relational dimension through the study of forums networks and patterns of interaction among participants in those networks, 2) the dimension relative to the process of teaching and learning through content analysis of the interviews; 3) and finally the dimension related to the participants' perceptions about the use of LMS for educational purposes and as a platform for creating social networks through the analysis of questionnaires.With the results obtained we carried out a comparative study between the four courses and tried to present a reflection on the Online Project of the University as well as possible causes that led to what was observed. We have finished with a proposal of a framework for studying the relational aspects of online learning networks aimed at possible future research in this area.
29
Trier, Matthias. "Towards a Social Network Intelligence Tool for visual Analysis of Virtual Communication Networks." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-140161.
Full textAbstract:
Communities of Practice regularly utilize virtual means of communication. The according software support provides its members with many sophisticated features for generating content and for communicating with each other via the internet or intranet. However, functionalities to monitor, assess, coordinate, and communicate the quality and development of the underlying electronic networks of experts are frequently missing. To meet this need of increased manageability, this contribution introduces a Social Network Intelligence software approach which aims at supporting the comprehension of the structure and value of electronic communities by automatically extracting and mining available electronic data of various types of virtual communication networks, like e-mail archives, discussion groups, or instant messaging communication. Experimental structural visualizations employing Social Network Analysis methods are combined with Keyword Extraction to move towards a Social Network Intelligence approach which generates transparency of complex virtual communication networks. Together with a comprehensive visualization method, an approach for software-supported communication network measurement and evaluation is suggested. It supports the identification of important participants, topics, or clusters in the network, evaluates the interpersonal communication structure and visually traces the evolvement of the knowledge exchange over time.
30
Trier, Matthias. "Towards a Social Network Intelligence Tool for visual Analysis of Virtual Communication Networks." Technische Universität Dresden, 2006. https://tud.qucosa.de/id/qucosa%3A27871.
Full textAbstract:
Communities of Practice regularly utilize virtual means of communication. The according software support provides its members with many sophisticated features for generating content and for communicating with each other via the internet or intranet. However, functionalities to monitor, assess, coordinate, and communicate the quality and development of the underlying electronic networks of experts are frequently missing. To meet this need of increased manageability, this contribution introduces a Social Network Intelligence software approach which aims at supporting the comprehension of the structure and value of electronic communities by automatically extracting and mining available electronic data of various types of virtual communication networks, like e-mail archives, discussion groups, or instant messaging communication. Experimental structural visualizations employing Social Network Analysis methods are combined with Keyword Extraction to move towards a Social Network Intelligence approach which generates transparency of complex virtual communication networks. Together with a comprehensive visualization method, an approach for software-supported communication network measurement and evaluation is suggested. It supports the identification of important participants, topics, or clusters in the network, evaluates the interpersonal communication structure and visually traces the evolvement of the knowledge exchange over time.
31
Cimenler, Oguz. "Social Network Analysis of Researchers' Communication and Collaborative Networks Using Self-reported Data." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5201.
Full textAbstract:
This research seeks an answer to the following question: what is the relationship between the structure of researchers' communication network and the structure of their collaborative output networks (e.g. co-authored publications, joint grant proposals, and joint patent applications), and the impact of these structures on their citation performance and the volume of collaborative research outputs? Three complementary studies are performed to answer this main question as discussed below.
1. Study I: A frequently used output to measure scientific (or research) collaboration is co-authorship in scholarly publications. Less frequently used are joint grant proposals and patents. Many scholars believe that co-authorship as the sole measure of research collaboration is insufficient because collaboration between researchers might not result in co-authorship. Collaborations involve informal communication (i.e., conversational exchange) between researchers. Using self-reports from 100 tenured/tenure-track faculty in the College of Engineering at the University of South Florida, researchers' networks are constructed from their communication relations and collaborations in three areas: joint publications, joint grant proposals, and joint patents. The data collection: 1) provides a rich data set of both researchers' in-progress and completed collaborative outputs, 2) yields a rating from the researchers on the importance of a tie to them 3) obtains multiple types of ties between researchers allowing for the comparison of their multiple networks. Exponential Random Graph Model (ERGM) results show that the more communication researchers have the more likely they produce collaborative outputs. Furthermore, the impact of four demographic attributes: gender, race, department affiliation, and spatial proximity on collaborative output relations is tested. The results indicate that grant proposals are submitted with mixed gender teams in the college of engineering. Besides, the same race researchers are more likely to publish together. The demographics do not have an additional leverage on joint patents.
2. Study II: Previous research shows that researchers' social network metrics obtained from a collaborative output network (e.g., joint publications or co-authorship network) impact their performance determined by g-index. This study uses a richer dataset to show that a scholar's performance should be considered with respect to position in multiple networks. Previous research using only the network of researchers' joint publications shows that a researcher's distinct connections to other researchers (i.e., degree centrality), a researcher's number of repeated collaborative outputs (i.e., average tie strength), and a researchers' redundant connections to a group of researchers who are themselves well-connected (i.e., efficiency coefficient) has a positive impact on the researchers' performance, while a researcher's tendency to connect with other researchers who are themselves well-connected (i.e., eigenvector centrality) had a negative impact on the researchers' performance. The findings of this study are similar except that eigenvector centrality has a positive impact on the performance of scholars. Moreover, the results demonstrate that a researcher's tendency towards dense local neighborhoods (as measured by the local clustering coefficient) and the researchers' demographic attributes such as gender should also be considered when investigating the impact of the social network metrics on the performance of researchers.
3. Study III: This study investigates to what extent researchers' interactions in the early stage of their collaborative network activities impact the number of collaborative outputs produced (e.g., joint publications, joint grant proposals, and joint patents). Path models using the Partial Least Squares (PLS) method are run to test the extent to which researchers' individual innovativeness, as determined by the specific indicators obtained from their interactions in the early stage of their collaborative network activities, impacts the number of collaborative outputs they produced taking into account the tie strength of a researcher to other conversational partners (TS). Within a college of engineering, it is found that researchers' individual innovativeness positively impacts the volume of their collaborative outputs. It is observed that TS positively impacts researchers' individual innovativeness, whereas TS negatively impacts researchers' volume of collaborative outputs. Furthermore, TS negatively impacts the relationship between researchers' individual innovativeness and the volume of their collaborative outputs, which is consistent with `Strength of Weak Ties' Theory. The results of this study contribute to the literature regarding the transformation of tacit knowledge into explicit knowledge in a university context.
32
Franco, Alessia <1996>. "How Blockchain Technology Can Help Rearchitect Social Networks: An Analysis of Desmos Network." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/19800.
Full textAbstract:
Centralized Social Networks (CSNs) such as Facebook, Instagram and Twitter are operated in an obscure, opaque and autocratic manner; many problems arise from centralized corporate power, these issues are based on the misalignment between profit-seeking incentives of corporations and user goals. Censorship, banning and data breaches have become increasingly frequent in the past five years, recent political tensions and the outbreak of Covid-19 have worsened this situation globally.
The main issue has an inborn nature: the business model of CSNs utilizes and monetizes users’ data to maximize the profitability of tech giants. In this thesis I explore the case of Desmos, which proposes an alternative approach to social media creation and management. Based on the belief that that the root cause of this problem is the broken relationship between the different parties, Desmos offers a project to fix this broken relationship using blockchain technology. Through a redesigned lifecycle and an improved economic model it can achieve the network growth required for a self-sustainable social network, without the need for centralized intermediaries, thus prioritizing the interests of users. Desmos is therefore the protocol that can be used to build decentralized social networks, while Desmos Token (DSM) is the native token of this blockchain, which allows the holders to contribute to the security and governance of the platform in exchange for incentives.
33
Orkins, William R., and Carla A. Kiernan. "COREnet: the fusion of social network analysis and target audience analysis." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/44638.
Full textAbstract:
Approved for public release; distribution is unlimitedThe purpose of this capstone is to highlight and explain how the target audience analysis (TAA) process can be enhanced by incorporating aspects of influence theory, social movement theory (SMT) and social network analysis (SNA). While a large body of literature addresses influence theory, SMT and SNA, little has been written within military information support operations (MISO) doctrine recognizing SNA in the analytical process. This capstone creates a method to apply SNA, SMT, and influence theory to existing MISO doctrine while also developing a scalable web-based application that assists with visualizing and analyzing open source data to draw meaningful conclusions and assist decision making on given operational problem sets. The web-based interface, COREnet, is a high fidelity prototype derived completely from open- source technology. The examples utilized are from a 2006 data set of an Indonesian terrorist network to demonstrate how SNA can be fully integrated into the TAA process.
34
Broccatelli, Chiara. "Going beyond secrecy : methodological advances for two-mode temporal criminal networks with Social Network Analysis." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/going-beyond-secrecy-methodological-advances-for-twomode-temporal-criminal-networks-with-social-network-analysis(f0f91f79-7bc3-442c-a16b-e9cf44cc68c3).html.
Full textAbstract:
This thesis seeks to extend the application of Social Network Analysis (SNA) to temporal graphs, in particular providing new insights for the understanding of covert networks. The analyses undertaken reveal informative features and properties of individuals' affiliations under covertness that also illustrate how both individuals and events influence the network structure. The review of the literature on covert networks provided in the initial two chapters suggests the presence of some ambiguities concerning how authors define structural properties and dynamics of covert networks. Authors sometimes disagree and use their findings to explain opposite views about covert networks. The controversy in the field is used as a starting point in order to justify the methodological application of SNA to understand how individuals involved in criminal and illegal activities interact with each other. I attempt to use a deductive approach, without preconceived notions about covert network characteristics. In particular, I avoid considering covert networks as organisations in themselves or as cohesive groups. I focus on individuals and their linkages constructed from their common participation in illicit events such as secret meetings, bombing attacks and criminal operations. In order to tackle these processes I developed innovative methods for investigating criminals' behaviours over time and their willingness to exchange tacit information. The strategy implies the formulation of a network model in order to represent and incorporate in a graph three types of information: individuals, events, and the temporal dimension of events. The inclusion of the temporal dimension offers the possibility of adopting a more comprehensive theoretical framework for considering individuals and event affiliations. This thesis expands the analysis of bipartite covert networks by adopting several avenues to explore in this perspective. Chapter 3 proposes a different way to represent two-mode networks starting from the use of line-graphs, namely the bi-dynamic line-graph data representation (BDLG), through which it is possible to represent the temporal evolution of individual's trajectories. The following chapter 4 presents some reflections about the idea of cohesion and cohesive subgroups specific to the case of two-mode networks. Based on the affiliation matrices, the analysis of local clustering through bi-cliques offers an attempt to analyse the mechanism of selecting accomplices while taking into account time. Chapter 5 is concerned with the concept of centrality of individuals involved in flows of knowledge exchanges. The theoretical and analytical framework helps in elaborating how individuals share their acquired hands-on experiences with others by attending joint task activities over time. Chapter 6 provides an application of the approaches introduced in the preceding chapters to the specific case of the Noordin Top terrorist network. Here, the knowledge of experience flow centrality measure opens up a new way to quantify the transmission of information and investigate the formation of the criminal capital. Finally, the last Chapter 7 presents some future research extensions by illustrating the versatility of the proposed approaches in order to provide new insights for the understanding of criminals' behaviours.
35
Rubano, Vincent. "Social network analysis| Determining betweenness centrality of a network using Ant Colony Optimization." Thesis, Southern Connecticut State University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10108549.
Full textAbstract:
Betweenness centrality refers to the measure of a node’s influence on the transfer of items within a network. It is a mechanism used to identify participants within an interconnected system that are responsible for processing high frequencies of traffic. This thesis examines the performance characteristics of a specialized artificial intelligence algorithm known as Ant Colony Optimization and its application in the field of social network analysis. The modeling and examination of such algorithms is important largely because of its ability to span across multiple fields of study as well as a variety of network applications. The effects of network analysis can be felt everywhere. Business and military intelligence; hardware resiliency (fault tolerance); network routing, are but a few of the fields that can and do benefit from research due in part to specialized network analysis. In this research paper, extensive social networks are built, execution time is measured, and algorithm viability is tested through the identification of high frequency nodes within real social networks.
36
Rocco, Giulia Eleonora <1990>. "L'incidenza dei Social Media nell'e-commerce e utilità della Social Network Analysis." Master's Degree Thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/5287.
Full text
37
Isah, Haruna. "Social Data Mining for Crime Intelligence: Contributions to Social Data Quality Assessment and Prediction Methods." Thesis, University of Bradford, 2017. http://hdl.handle.net/10454/16066.
Full textAbstract:
With the advancement of the Internet and related technologies, many traditional crimes have made the leap to digital environments. The successes of data mining in a wide variety of disciplines have given birth to crime analysis. Traditional crime analysis is mainly focused on understanding crime patterns, however, it is unsuitable for identifying and monitoring emerging crimes. The true nature of crime remains buried in unstructured content that represents the hidden story behind the data. User feedback leaves valuable traces that can be utilised to measure the quality of various aspects of products or services and can also be used to detect, infer, or predict crimes. Like any application of data mining, the data must be of a high quality standard in order to avoid erroneous conclusions. This thesis presents a methodology and practical experiments towards discovering whether (i) user feedback can be harnessed and processed for crime intelligence, (ii) criminal associations, structures, and roles can be inferred among entities involved in a crime, and (iii) methods and standards can be developed for measuring, predicting, and comparing the quality level of social data instances and samples. It contributes to the theory, design and development of a novel framework for crime intelligence and algorithm for the estimation of social data quality by innovatively adapting the methods of monitoring water contaminants. Several experiments were conducted and the results obtained revealed the significance of this study in mining social data for crime intelligence and in developing social data quality filters and decision support systems.
38
Jenelius, Erik. "Approaches to road network vulnerability analysis." Licentiate thesis, Stockholm : Infrastruktur, Kungliga Tekniska högskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4518.
Full text
39
Lospinoso, Joshua Alfred. "Statistical models for social network dynamics." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:d5ed9b9c-020c-4379-a5f2-cf96439ca37c.
Full textAbstract:
The study of social network dynamics has become an increasingly important component of many disciplines in the social sciences. In the past decade, statistical models and methods have been proposed which permit researchers to draw statistical inference on these dynamics. This thesis builds on one such family of models, the stochastic actor oriented model (SAOM) proposed by Snijders [2001]. Goodness of fit for SAOMs is an area that is only just beginning to be filled in with appropriate methods. This thesis proposes a Mahalanobis distance based, Monte Carlo goodness of fit test that can depend on arbitrary features of the observed network data and covariates. As remediating poor fit can be a difficult process, a modified model distance (MMD) estimator is devised that can help researchers to choose among a set of model elaborations. In practice, panel data is typically used to draw SAOM-based inference. This thesis also proposes a score-type test for time heterogeneity between the waves in the panel that is computationally cheap and fits into a convenient, forward model selecting workflow. Next, this thesis proposes a rigorous method for aggregating so-called relational event data (e.g. emails and phone calls) by extending the SAOM family to a family of hidden Markov models that suppose a latent social network is driving the observed relational events. Finally, this thesis proposes a measurement model for SAOMs inspired by error-in-variables (EiV) models employed in an array of disciplines. Like the relational event aggregation model, the measurement model is a hidden Markov model extension to the SAOM family. These models allow the researcher to specify the form of the mesurement error and buffer against potential attenuating biases and other problems that can arise if the errors are ignored.
40
Eiesland, Jon Wostryck. "Communities in a large social network : visualization and analysis." Thesis, Norwegian University of Science and Technology, Department of Physics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-6409.
Full textAbstract:
Communities have been a hot topic in complex network research the last years. Several algorithms for detecting communities have been developed, and in this thesis we use the sequential clique percolation algorithm to detect communities in a large social network. Our network consists of 5.3 million mobile phone users, with mutual communication data aggregated over 18 weeks.
In this thesis we do a visual study of the communities, and we clearly see the nested community structure when we do clique percolation for dierent clique sizes. When we threshold the edge weights we see that the strongest edges are in the densest subcommunities and that the weakest edges keep the communities connected.
We also present numerical analysis of some selected structure and topology properties of the communities. Lastly we confirm, by numerical analysis of the available demographic data on the mobile phone users, that the communities are more conform with respect to zip code, age and sex compared to a reference network where the demographic attributes have been shuffled.
Samfunn har vært et hett emne innen forskning på komplekse nettverk de siste årene. Det har blitt utviklet flere algoritmer for å finne samfunn, og i denne oppgaven bruker vi sekvensiell klikkperkolasjon til å finne samfunn i et stort sosialt nettverk. Nettverket vårt består av 5.3 millioner mobiltelefonbrukere, med gjensidig kommunikasjonsdata aggregert over 18 uker.
I denne oppgaven gjør vi en visuell studie av samfunnene, og vi ser tydelig den vevde sammfunnsstrukturen når vi utfører klikkperkolasjon for ulike klikkstørrelser. Når vi setter terskler for lenkevektene ser vi at de sterkeste lenkene er i de tetteste undersamfunnene og at de svakeste lenkene holder samfunnene i kontakt med hverandre.
Vi presenterer også en numerisk analyse av noen utvalgte struktur- og topologiegenskaper hos samfunnene. Til slutt bekrefter vi, via numerisk analyse av den tilgjengelige demografiske informasjonen om mobiltelefonbrukerne, at samfunnene er mer konforme med tanke på postkode, alder og kjønn sammenlignet med et referansenettverk hvor de demografiske attributtene har blitt stokket om.
41
Zheng, Ju Kimberly. "A Social Network Analysis of Corporate Venture Capital Syndication." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/854.
Full textAbstract:
The importance of social capital can be characterized by a well-known quote: "it's not just what you know, but whom you know". Firms with rich social capital are more informed, more capable, and more competitive, because networks of resources are within their reach. Social capital is embedded in social networks, and social network analysis is the chief topic of this research. The network being examined contains 1126 venture capital (VC) programs, 206 of them being corporate venture programs, and the rest consisting of independent venture capital firms. Venture programs co-invest in portfolio firms following an identifiable pattern. This research attempts to explain this co-investment pattern using social network analysis. Four attributes of social networks are explored during this analysis: prominence, range, brokerage, and cohesion. The findings of the corporate venture capital network provide a number of implications for the theory of social capital. The objective of the thesis is using social capital to examine the syndication patterns in a corporate VC network. The analysis of the corporate VC co-investment pattern supports four hypotheses. First, the corporate VC network is not cohesive. Second, most relationships in the network are indirect. Third, most prominent VCs are also the most powerful resource brokers in the network. Lastly, prominent VCs are likely to syndicate with other prominent VCs.
42
Chandler, Kathryn Suzanne. "Exploring the principle of provenance with social network analysis." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/57849.
Full textAbstract:
Traditionally, an archival fonds is conceptualized as an aggregate of records which are mutually relevant. This mutual relevance is often attributed to the origin of member records in a common context – with this context typically understood as the context of an organization, and more specifically, a department. It is considered difficult to identify mutually relevant records in modern organizations. This difficulty is often attributed to frequent administrative changes which disrupt departmental contexts. This thesis tests a technique that aims to use the information within the records to identify a context common to a set of records. It involves extracting the name of the creator and the name of the modifier from each record, then subjecting this information to a community detection algorithm. It was hypothesized that groups of individuals who frequently modify one another’s records constitute a common context. After applying various community detection algorithms to the records of an organization, the resulting groups of records were presented to the staff of the organization for feedback. Staff clearly indicated that groups of records produced by the community detection algorithms were not mutually relevant. These results can be explained with reference to the works of Jenny Bunn, who argued that an autonomous community only comes into existence when constituent members engage in both “being” and “doing.” During the interviews with staff, it was clear that some algorithms produced groups of people characterized by established relationships (“being”) while others produced groups in pursuit of a joint activity (“doing”). The absence of overlap suggests there were no autonomous subcommunities in this study, and therefore, no common context by which records can be bound. Mutually relevant records can also be formed by employees in their attempts to keep records orderly. To explore this further, it was argued that constructing a folder structure is akin to constructing a narrative, with the narrative components taking the form of records. When numerous employees attempt to organize the same records using different narratives, the aggregate may seem disorderly. This thesis suggests that disentangling these narratives is a method by which order may be restored.Arts, Faculty of
Library, Archival and Information Studies (SLAIS), School of
Graduate
43
Maier, Gunther, and Michael Vyborny. "Internal migration between US-states. A social network analysis." Institut für Regional- und Umweltwirtschaft, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/1084/1/document.pdf.
Full textAbstract:
In this paper we use the novel (at least in regional science) technique of social network analysis and apply it to one of the most analyzed topics in the discipline, US internal migration. We want to see whether social network analysis can yield any new insights into this well known process. We want to compare the technique to more conventional methods of analysis in migration. The paper presents an overview of social network analysis, defines key concepts and describes the main components of the technique. This discussion will also involve a discussion of currently available software for social network analysis. Then, we will apply the technique to the official data about internal migration between US states as published by the US bureau of the census, to see whether the technique can reproduce the main results of the traditional techniques and whether it can yield any new insights.Series: SRE - Discussion Papers
44
Lau, Dora C. S. "Job consequences of trustworthy employees, a social network analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ61133.pdf.
Full text
45
Adjodah, Dhaval D. K. (Adjodlah Dhaval Dhamnidhi Kumar). "Understanding social influence using network analysis and machine learning." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81111.
Full textAbstract:
Thesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 61-62).
If we are to enact better policy, fight crime and decrease poverty, we will need better computational models of how society works. In order to make computational social science a useful reality, we will need generative models of how social influence sprouts at the interpersonal level and how it leads to emergent social behavior. In this thesis, I take steps at understanding the predictors and conduits of social influence by analyzing real-life data, and I use the findings to create a high-accuracy prediction model of individuals' future behavior. The funf dataset which comprises detailed high-frequency data gathered from 25 mobile phone-based signals from 130 people over a period of 15 months, will be used to test the hypothesis that people who interact more with each other have a greater ability to influence each other. Various metrics of interaction will be investigated such as self-reported friendships, call and SMS logs and Bluetooth co-location signals. The Burt Network Constraint of each pair of participants is calculated as a measure of not only the direct interaction between two participants but also the indirect friendships through intermediate neighbors that form closed triads with both the participants being assessed. To measure influence, the results of the live funf intervention will be used where behavior change of each participant to be more physically active was rewarded, with the reward being calculated live. There were three variants of the reward structure: one where each participant was rewarded for her own behavior change without seeing that of anybody else (the control), one where each participant was paired up with two 'buddies' whose behavior change she could see live but she was still rewarded based on her own behavior, and one where each participant who was paired with two others was paid based on their behavior change that she could see live. As a metric for social influence, it will be considered how the change in slope and average physical activity levels of one person follows the change in slope and average physical activity levels of the buddy who saw her data and/or was rewarded based on her performance. Finally, a linear regression model that uses the various types of direction and indirect network interactions will be created to predict the behavior change of one participant based on her closeness with her buddy. In addition to explaining and demonstrating the causes of social influence with unprecedented detail using network analysis and machine learning, I will discuss the larger topic of using such a technology-driven approach to changing behavior instead of the traditional policy-driven approach. The advantages of the technology-driven approach will be highlighted and the potential political-economic pitfalls of implementing such a novel approach will also be addressed. Since technology-driven approaches to changing individual behavior can have serious negative consequences for democracy and the free-market, I will introduce a novel dimension to the discussion of how to protect individuals from the state and from powerful private organizations. Hence, I will describe how transparency policies and civic engagement technologies can further this goal of 'watching the watchers'.
by Dhaval D.K. Adjodah.
S.M.in Technology and Policy
46
Zhang, Kunpeng, Siddhartha Bhattacharyya, and Sudha Ram. "LARGE-SCALE NETWORK ANALYSIS FOR ONLINE SOCIAL BRAND ADVERTISING." SOC INFORM MANAGE-MIS RES CENT, 2016. http://hdl.handle.net/10150/623353.
Full textAbstract:
This paper proposes an audience selection framework for online brand advertising based on user activities on social media platforms. It is one of the first studies to our knowledge that develops and analyzes implicit brand-brand networks for online brand advertising. This paper makes several contributions. We first extract and analyze implicit weighted brand-brand networks, representing interactions among users and brands, from a large dataset. We examine network properties and community structures and propose a framework combining text and network analyses to find target audiences. As a part of this framework, we develop a hierarchical community detection algorithm to identify a set of brands that are closely related to a specific brand. This latter brand is referred to as the "focal brand." We also develop a global ranking algorithm to calculate brand influence and select influential brands from this set of closely related brands. This is then combined with sentiment analysis to identify target users from these selected brands. To process large-scale datasets and networks, we implement several MapReduce-based algorithms. Finally, we design a novel evaluation technique to test the effectiveness of our targeting framework. Experiments conducted with Facebook data show that our framework provides significant performance improvements in identifying target audiences for focal brands.
47
Buchanan, Courtney Nicole, and Courtney Nicole Buchanan. "Network Analysis of “C” Level Executives on Social Media." Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/624931.
Full textAbstract:
Twitter is becoming a much more common platform for all age groups and backgrounds. It provides an avenue to connect millions of people from diverse experiences and interests. The following thesis explores this connectivity between businesspeople across the world. It studies the relationships between 345 "C" level executives with a Twitter presence. This paper also researches the factors that form communities as well as what profile characteristics makes an account more popular. Metrics of measurement for social networks are defined and applied to this selected network. Qualitative profile information as well as lists of each of the accounts the 345 users are following are analyzed via visualization and exploration software to gain a better understanding of these popular profiles. Ultimately, this thesis seeks to determine if similar interests, popularity and reputation, and verified profiles influence the popularity of an account in a network.
48
Narayanam, Ramasuri. "Game Theoretic Models For Social Network Analysis." Thesis, 2011. https://etd.iisc.ac.in/handle/2005/2350.
Full textAbstract:
With increasing demand for social network based activities, it is very important to understand not only the structural properties of social networks but also how social networks form, to better exploit their promise and potential. We believe the existing methods and tools for social network analysis have a major inadequacy: they do not capture the behavior (such as rationality and intelligence) of individuals nor do they model the strategic interactions that occur among these individuals. Game theory is a natural tool to overcome this inadequacy since it provides rigorous mathematical models of strategic interaction among autonomous, intelligent, and rational agents. This thesis brings out how a game theoretic approach helps analyze social networks better. In particular, we study three contemporary and pertinent problems in social networks using a game theoretic approach: determining influential individuals for viral marketing, community detection, and social network formation.
The first problem deals with determining influential nodes in social networks for diffusion of information. We present an efficient heuristic algorithm (SPIN) to this problem based on cooperative game theoretic techniques. The running time of SPIN is independent of the number of influential nodes to be determined. Moreover, unlike the popular benchmark algorithms, the proposed method works well with both submodular and non-submodular objective functions for diffusion of information.
In the second problem, we design a novel game theoretic approach to partition a given undirected, unweighted graph into dense subgraphs (or communities). The approach is based on determining a Nash stable partition which is a pure strategy Nash equilibrium of an appropriately defined strategic form game. In the proposed graph partitioning game, the nodes of the graph are the players and the strategy of a node is to decide to which community it ought to belong. The utility of each node is defined to depend entirely on the node’s local neighborhood. A Nash stable partition (NSP) of this game is a partition consisting of communities such that no node has incentive to defect from its community to any other community. Given any graph, we prove that an NSP always exists and we also derive a lower bound on the fraction of intra-community edges in any NSP. Our approach leads to an efficient heuristic algorithm to detect communities in social networks with the additional feature of automatically determining the number of communities.
The focus of the third problem is to understand the patterns behind the evolution of social networks that helps in predicting the likely topologies of social networks. The topology of social networks plays a crucial role in determining the outcomes in several social and economic situations such as trading networks, recommendation networks. We approach the problem of topology prediction in networks by defining a game theoretic model, which we call value function -allocation rule model, that considers four determinants of network formation. This model uses techniques from both cooperative game theory and non-cooperative game theory. We characterize the topologies of networks that are in equilibrium and/or socially efficient. Finally, we study the tradeoffs between equilibrium networks and efficient networks.
49
Narayanam, Ramasuri. "Game Theoretic Models For Social Network Analysis." Thesis, 2011. http://etd.iisc.ernet.in/handle/2005/2350.
Full textAbstract:
With increasing demand for social network based activities, it is very important to understand not only the structural properties of social networks but also how social networks form, to better exploit their promise and potential. We believe the existing methods and tools for social network analysis have a major inadequacy: they do not capture the behavior (such as rationality and intelligence) of individuals nor do they model the strategic interactions that occur among these individuals. Game theory is a natural tool to overcome this inadequacy since it provides rigorous mathematical models of strategic interaction among autonomous, intelligent, and rational agents. This thesis brings out how a game theoretic approach helps analyze social networks better. In particular, we study three contemporary and pertinent problems in social networks using a game theoretic approach: determining influential individuals for viral marketing, community detection, and social network formation.
The first problem deals with determining influential nodes in social networks for diffusion of information. We present an efficient heuristic algorithm (SPIN) to this problem based on cooperative game theoretic techniques. The running time of SPIN is independent of the number of influential nodes to be determined. Moreover, unlike the popular benchmark algorithms, the proposed method works well with both submodular and non-submodular objective functions for diffusion of information.
In the second problem, we design a novel game theoretic approach to partition a given undirected, unweighted graph into dense subgraphs (or communities). The approach is based on determining a Nash stable partition which is a pure strategy Nash equilibrium of an appropriately defined strategic form game. In the proposed graph partitioning game, the nodes of the graph are the players and the strategy of a node is to decide to which community it ought to belong. The utility of each node is defined to depend entirely on the node’s local neighborhood. A Nash stable partition (NSP) of this game is a partition consisting of communities such that no node has incentive to defect from its community to any other community. Given any graph, we prove that an NSP always exists and we also derive a lower bound on the fraction of intra-community edges in any NSP. Our approach leads to an efficient heuristic algorithm to detect communities in social networks with the additional feature of automatically determining the number of communities.
The focus of the third problem is to understand the patterns behind the evolution of social networks that helps in predicting the likely topologies of social networks. The topology of social networks plays a crucial role in determining the outcomes in several social and economic situations such as trading networks, recommendation networks. We approach the problem of topology prediction in networks by defining a game theoretic model, which we call value function -allocation rule model, that considers four determinants of network formation. This model uses techniques from both cooperative game theory and non-cooperative game theory. We characterize the topologies of networks that are in equilibrium and/or socially efficient. Finally, we study the tradeoffs between equilibrium networks and efficient networks.
50
Naimisha, Kolli. "Applications Of Social Network Analysis To Community Dynamics." Thesis, 2008. https://etd.iisc.ac.in/handle/2005/834.
Full textAbstract:
This thesis concerns Social Network Analysis as a mechanism for exploring Community Dynamics. To be able to use the Social Network methodologies, relationships existing between the modeling entities are required. In this thesis, we use two different kinds of relationships: e-mails exchanged and co-authorship of papers. The e-mails exchanged, as an indicator of information exchange in an organization, is used to facilitate the emergence of structure within the organization. In this thesis we demonstrate the effectiveness of using e-mail communication patterns for crisis detection in a hierarchically set organization. We compare the performance of a Social Network based Classifier with some of the traditional classifiers from the data mining framework for inferring this hierarchy. A generic framework for studying dynamic group transformations is presented and the co-authorship of papers, as an indicator of collaboration in an academic institution, is used to study the community behavioral patterns evolving over time. Enron e-mail corpus and the IISc Co-authorship Dataset are utilized for illustrative purposes.