Stochastic Network Formation and Homophily

Pin, Paolo; Rogers, Brian W.

doi:10.1093/oxfordhb/9780199948277.013.6

Cited by 14 publications

(7 citation statements)

References 113 publications

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“…22 There is also an important literature on dynamic networks. For recent surveys, seeÖzgür (2011) and Pin and Rogers (2016). 23 Papers that contribute to this line of modeling and derive such a result include Geoffard and Philipson (1997), Kremer (1996), Toxvaerd (2012), and Galeotti and Rogers (2015).…”

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confidence: 99%

The Economic Consequences of Social-Network Structure

Jackson

Rogers

Zénou

2017

Journal of Economic Literature

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358

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We survey the literature on the economic consequences of the structure of social networks. We develop a taxonomy of 'macro' and 'micro' characteristics of social interaction networks and discuss both the theoretical and empirical findings concerning the role of those characteristics in determining learning, diffusion, decisions, and resulting behaviors. We also discuss the challenges of accounting for the endogeneity of networks in assessing the relationship between the patterns of interactions and behaviors.

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confidence: 99%

The Economic Consequences of Social-Network Structure

Jackson

Rogers

Zénou

2017

Journal of Economic Literature

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358

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“…The difference between the approach based on exact versus stochastic network information is illustrated in Figure 3. In the next section we review some commonly used stochastic network formation models, more details and extensions can be found in Bollobás and Béla (2001); Jackson (2010); Pin and Rogers (2015); Newman et al (2001); Newman (2010). We then illustrate in Sections 3.2 and 3.3 how these models have been used to study sampled network games with continuous and discrete action sets, respectively.…”

Section: Sampled Networkmentioning

confidence: 99%

Analysis and Interventions in Large Network Games

Parise

Ozdaglar

2021

Annu. Rev. Control Robot. Auton. Syst.

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We review classic results and recent progress on equilibrium analysis, dynamics, and optimal interventions in network games with both continuous and discrete strategy sets. We study strategic interactions in deterministic networks as well as networks generated from a stochastic network formation model. For the former case, we review a unifying framework for analysis based on the theory of variational inequalities. For the latter case, we highlight how knowledge of the stochastic network formation model can be used by a central planner to design interventions for large networks in a computationally efficient manner when exact network data are not available. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…On the one hand, homophily and the requirement of social reinforcement for behavioral adoption in complex contagion theory indicate that influence tends to be localized in homophilous communities [12,30]. In other words, behavioral diffusion and network formation are endogenous, explaining the phenomenon of within-community spreading [34,39]. On the other hand, the weak ties theory [19] implies that bridging ties between communities facilitate the spreading of novel ideas.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Stochastic Block Influence Model: measuring social influence among homophilous communities

Leng¹,

Sowrirajan²,

Pentland³

2020

Preprint

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Decision-making on networks can be explained by both homophily and social influence. While homophily drives the formation of communities with similar characteristics, social influence occurs both within and between communities. Social influence can be reasoned through role theory, which indicates that the influences among individuals depend on their roles and the behavior of interest. To operationalize these social science theories, we empirically identify the homophilous communities and use the community structures to capture the "roles", which affect the particular decision-making processes. We propose a generative model named Stochastic Block Influence Model and jointly analyze both the network formation and the behavioral influence within and between different empirically-identified communities. To evaluate the performance and demonstrate the interpretability of our method, we study the adoption decisions of microfinance in an Indian village. We show that although individuals tend to form links within communities, there are strong positive and negative social influences between communities, supporting the weak tie theory. Moreover, we find that communities with shared characteristics are associated with positive influence. In contrast, the communities with a lack of overlap are associated with negative influence. Our framework facilitates the quantification of the influences underlying decision communities and is thus a useful tool for driving information diffusion, viral marketing, and technology adoptions.

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Stochastic Network Formation and Homophily

Cited by 14 publications

References 113 publications

The Economic Consequences of Social-Network Structure

The Economic Consequences of Social-Network Structure

Analysis and Interventions in Large Network Games

Interpretable Stochastic Block Influence Model: measuring social influence among homophilous communities

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