Social Structure of Facebook Networks

By Amanda L. Traud, Peter J. Mucha, M. A. Porter. arXiv.org, 2011

811 citations recorded by the source corpus.

We study the social structure of Facebook “friendship” networks at one hundred American colleges and universities at a single point in time, and we examine the roles of user attributes–gender, class year, major, high school, and residence–at these institutions. We investigate the influence of common attributes at the dyad level in terms of assortativity coefficients and regression models. We then examine larger-scale groupings by detecting communities algorithmically and comparing them to network partitions based on user characteristics. We thereby examine the relative importance of different characteristics at different institutions, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions. Our calculations illustrate how microscopic and macroscopic perspectives give complementary insights on the social organization at universities and suggest future studies to investigate such phenomena further.

Subfields: Communication, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/4e108f9eefc36525e121c236234d9658c7aa9bbe

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Citations in the corpus

Estimation of Simultaneously Sparse and Low Rank Matrices (2012, methodology): “4 0.02 Table 1. Prediction of interactions in Escherichia coli. Mean normalized RMSE and standard deviations. Social Networks. We have performed experiments with the Facebook100 data set analyzed by (Traud et al., 2011). The data set comprises all friendship relations between students aliated to a s”
The Anatomy of the Facebook Social Graph (2011, background): “Some of this research has been devoted towards understanding small subsets of the Facebook population, including in particular the social networks of university students [17–19].”
Community detection in networks: A user guide (2016, background): “The networks represent FaceBook friendships between students of US universities [135].”
Local Higher-Order Graph Clustering (2017, background): “on arXiv [30]; fb-Harvard1, a snapshot of the friendships network between Harvard students on Facebook in September 2005 [42]; email-Enron, an e-mail communication network of the employees”
Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding (2018, methodology): “Data Sets We employed the following three real datasets in Facebook social networks [Traud et al. , 2012] for vertex classiﬁcation, which comprises 100 colleges and universities in US.”
Attributed Social Network Embedding (2017, methodology): “For example, for the UNC dataset, only 4 , 018 of the 18 , 163 users contain all attributes (as plotted in Figure 1).”
Network Representation Learning: A Survey (2017, background): “The Amherst, Hamilton, Mich and Rochester [86] datasets are the Facebook networks formed by users from the corresponding US universities, where”
Community Preserving Network Embedding (2017, methodology): “For each user, there are six pieces of metadata, and according to (Traud, Mucha, and Porter 2012), the class year is used as the ground-truth of community structure.”
You Are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors (2016, background): “ributes for users that are not in any local communities. Moreover, this approach is data dependent since detected communities might not correlate with the attribute value. For instance, Trauda et al. [41] found that communities in a MIT male network are correlated with residence but a female network”
A Survey on Network Embedding (2017, methodology): “, Facebook [86] and YELP [59]), citation networks (e.”
Structure and inference in annotated networks (2015, background): “These results align well with the original analysis of the same data by Traud et al. [31], who performed a traditional community division of the network and then carried out post hoc statistical tests to measure correlations between communities and metadata.”
You are what you like! Information leakage through users' Interests (2012, background): “Computing the neighbors of each user in the feature space (i.e., whose IFVs are similar in the feature space) to discover similar users, and exploiting this neighborhood to infer hidden attributes.”
AttriInfer: Inferring User Attributes in Online Social Networks Using Markov Random Fields (2017, background): “For instance, Trauda et al. [29] found that communities in a MIT male network are correlated with residence but a female network does not have such property.”
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods (2021, neutral): “Using other Facebook 100 datasets besides Penn94 [67] would allow for inductive node classification, but LINKX does not directly generalize to this setting.”