Social structure of Facebook networks

Traud, Amanda L.; Mucha, Peter J.; Porter, Mason A.

doi:10.1016/j.physa.2011.12.021

Cited by 499 publications

(229 citation statements)

References 50 publications

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“…These pieces of metadata were used to form separate levels of groups. Networks were originally released by M. A. Porter [18] and are available on several sites on the web. The "gender" metadata were discarded from the analysis as they form one giant network-spanning group for male and female, with isolated fringes.…”

Section: Discussionmentioning

confidence: 99%

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Community detection in networks: Structural communities versus ground truth

2014

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Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (non-topological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes. We show that traditional community detection methods fail to find the metadata groups in many large networks. Our results show that there is a marked separation between structural communities and metadata groups, in line with recent findings. That means that either our current modeling of community structure has to be substantially modified, or that metadata groups may not be recoverable from topology alone.

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Section: Discussionmentioning

confidence: 99%

“…The dblp network of coauthorships in computer science literature has publication venues as groups [17]. The Facebook university networks (fb100) consist of 100 separate networks of Facebook users at US universities from 2005 [18]. The multiplicity was used to provide statistics.…”

Section: A Network Datasetsmentioning

confidence: 99%

Community detection in networks: Structural communities versus ground truth

2014

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“…For example, the topology of the social network, both in terms of connectivity and rate of information flow, could be based on realistic measurements of network traffic as measured by cellular communication [95], Twitter [96], or Facebook [97]. Or more generally, one could employ mechanistic models to construct networks whose topologies closely match those of empirical social networks.…”

Section: Future Directionsmentioning

confidence: 99%

Collective decision dynamics in the presence of external drivers

2012

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We develop a sequence of models describing information transmission and decision dynamics for a network of individual agents subject to multiple sources of influence. Our general framework is set in the context of an impending natural disaster, where individuals, represented by nodes on the network, must decide whether or not to evacuate. Sources of influence include a one-to-many externally driven global broadcast as well as pairwise interactions, across links in the network, in which agents transmit either continuous opinions or binary actions. We consider both uniform and variable threshold rules on the individual opinion as baseline models for decision making. Our results indicate that (1) social networks lead to clustering and cohesive action among individuals, (2) binary information introduces high temporal variability and stagnation, and (3) information transmission over the network can either facilitate or hinder action adoption, depending on the influence of the global broadcast relative to the social network. Our framework highlights the essential role of local interactions between agents in predicting collective behavior of the population as a whole.

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“…The third set of networks are empirical and deterministic. In particular, we use two Facebook100 networks, which are constructed using Facebook "friendship" data [51], and which are a type of network in which people have discussions and opinions can change over time. Using all three types of networks, we discuss the number of opinion groups at equilibrium and phenomena such as a confidence-bound threshold for a transition from consensus to multiple-opinion equilibria.…”

Section: Introductionmentioning

confidence: 99%

Opinion formation and distribution in a bounded-confidence model on various networks

2018

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In the social, behavioral, and economic sciences, it is an important problem to predict which individual opinions will eventually dominate in a large population, if there will be a consensus, and how long it takes a consensus to form. This idea has been studied heavily both in physics and in other disciplines, and the answer depends strongly on both the model for opinions and for the network structure on which the opinions evolve. One model that was created to study consensus formation quantitatively is the Deffuant model, in which the opinion distribution of a population evolves via sequential random pairwise encounters. To consider the heterogeneity of interactions in a population due to social influence, we study the Deffuant model on various network structures (deterministic synthetic networks, random synthetic networks, and social networks constructed from Facebook data) using several interaction mechanisms. We numerically simulate the Deffuant model and conduct regression analyses to investigate the dependence of the convergence time to equilibrium on parameters, including a confidence bound for opinion updates, the number of participating entities, and their willingness to compromise. We find that network structure and parameter values both have an effect on the convergence time, and for some network topologies, the convergence time undergoes a transition at a critical value of the confidence bound. We discuss the number of opinion groups that form at equilibrium in terms of a confidence-bound threshold for a transition from consensus to multiple-opinion equilibria.

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Social structure of Facebook networks

Cited by 499 publications

References 50 publications

Community detection in networks: Structural communities versus ground truth

Community detection in networks: Structural communities versus ground truth

Collective decision dynamics in the presence of external drivers

Opinion formation and distribution in a bounded-confidence model on various networks

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