Identifying Community Structures from Network Data via Maximum Likelihood Methods

Čopič, Jernej; Jackson, Matthew O.; Kirman, Alan

doi:10.2202/1935-1704.1523

Cited by 62 publications

(52 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…33 Essentially, there is homophily, but the researcher does not directly observe the communities that underly the homophily and so seeks to recover it. A class of such community detection techniques based on maximum likelihood estimation have been axiomatized by Copic, Jackson, and Kirman (2009).…”

Section: Community Detectionmentioning

confidence: 99%

An Overview of Social Networks and Economic Applications

Jackson¹

2011

Handbook of Social Economics

Self Cite

216

147

View full text Add to dashboard Cite

In this chapter I provide an overview of research on social networks and their role in shaping behavior and economic outcomes. I include discussion of empirical and theoretical analyses of the role of social networks in markets and exchange, learning and diffusion, and network games. I also include some background on social network characteristics and measurements, models of network formation, models for the statistical analysis of social networks, as well as community detection.

show abstract

Section: Community Detectionmentioning

confidence: 99%

An Overview of Social Networks and Economic Applications

Jackson¹

2011

Handbook of Social Economics

Self Cite

216

147

View full text Add to dashboard Cite

show abstract

“…The importance of social circles as building blocks in networks have been emphasized by a number of sociologists, see for example the review in Degenne et Forsé [13], chapter 2. We also find a vast literature that deals with algorithms that identify community structures in network data (minimum-cut, hierarchical clustering, see Johnson et al [27], or more recent methods such as that of Girvan et al [20] ) or Copic et al [12]. However, many of these assume that each individual belongs to a single community which is to be identified.…”

Section: The Network: Community Based Small Worlds Random Graphsmentioning

confidence: 97%

Diffusion of Innovations on Community Based Small Worlds: The Role of Correlation between Social Spheres

Tanimura

2010

SSRN Journal

View full text Add to dashboard Cite

SummaryWhich types of networks favor the diffusion of innovations in the sense that an innovation whose intrinsic benefits are greater than those of an established choice will be able to replace the latter when it is initially used only by a small fraction of a large population? For deterministic and regular networks there are characterizations, based on a coordination game model of the diffusion of innovations. Here we study this question for a class of irregular random networks, Small world networks, which are of interest as more realistic models of social networks. We consider a random graph model based on a community structure, in which the choice of a parameter allows us to obtain as special cases several well known models, in particular Watts' Small world. We show that there are different types of Small World graphs some which favor diffusion, others that do not. Our study suggests that the kinds of ties that exist between different communities of an individual play an important role. We interpret Watts' Small World as one with high correlation between social spheres of individuals and favorable to diffusion. In other Small Worlds where the communities of individuals are uncorrelated diffusion succeeds only for very large payoff benefits in favor of the innovation. Abstract: Which types of networks favor the diffusion of innovations in the sense that an innovation whose intrinsic benefits are greater than those of an established choice will be able to replace the latter when it is initially used only by a small fraction of a large population? For deterministic and regular networks there are characterizations, based on a coordination game model of the diffusion of innovations. Here we study this question for a class of irregular random networks, Small world networks, which are of interest as more realistic models of social networks. We consider a random graph model based on a community structure, in which the choice of a parameter allows us to obtain as special cases several well known models, in particular Watts' Small world. We show that there are different types of Small World graphs some which favor diffusion others that do not. Our study suggests that the kinds of ties that exist between different communities of an individual play an important role. We interpret Watts' Small World as one with high correlation between social spheres of individuals and favorable to diffusion. In other Small Worlds where the communities of individuals are uncorrelated diffusion succeeds only for very large payoff benefits in favor of the innovation. Keywords

show abstract

“…While that game turns out to be hard to analyze even in three-person examples, it was an important precursor to the more recent economic literature on network formation. 38 In contrast to the cooperative game setting, Jackson and Wolinsky (1996) explicitly considered networks, rather than coalitions, as the primitive. Thus rather than deducing utilities indirectly through a cooperative game on a graph, they posited that networks were the primitive structure and agents derived utilities based on the network structure in place.…”

Section: An Economic Approachmentioning

confidence: 99%

“…Many algorithms have been developed to identify community structures in networks, and yet most of what we know about the relative merits or de…ciencies of various approaches and algorithms comes simply from examining whether they seem to give the "right" community structuring in various examples. An overview of some of this literature can be found in Newman (2004b) and a discussion of the importance of identifying the properties for identifying community structures can be found in Copic, Jackson and Kirman (2005).…”

Section: Whither Now?mentioning

confidence: 99%

The Economics of Social Networks

Jackson¹

Advances in Economics and Econometrics

Self Cite

View full text Add to dashboard Cite

The science of social networks is a central …eld of sociological study, a major application of random graph theory, and an emerging area of study by economists, statistical physicists and computer scientists. While these literatures are (slowly) becoming aware of each other, and on occasion drawing from one another, they are still largely distinct in their methods, interests, and goals. Here, my aim is to provide some perspective on the research from these literatures, with a focus on the formal modeling of social networks and the two major types of models: those based on random graphs and those based on game theoretic reasoning. I highlight some of the strengths, weaknesses, and potential synergies between these two network modeling approaches. JEL Classi…cation Numbers: A14, C71, C72

show abstract

Identifying Community Structures from Network Data via Maximum Likelihood Methods

Cited by 62 publications

References 28 publications

An Overview of Social Networks and Economic Applications

An Overview of Social Networks and Economic Applications

Diffusion of Innovations on Community Based Small Worlds: The Role of Correlation between Social Spheres

The Economics of Social Networks

Contact Info

Product

Resources

About