Algorithms for clustering data
Algorithms for clustering data
Graph drawing by force-directed placement
Software—Practice & Experience
Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer Series in Statistics)
Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer Series in Statistics)
Pattern Recognition Letters
Comparing clusterings---an information based distance
Journal of Multivariate Analysis
Networks: An Introduction
Walking in facebook: a case study of unbiased sampling of OSNs
INFOCOM'10 Proceedings of the 29th conference on Information communications
Wedderburn Rank Reduction and Krylov Subspace Method for Tensor Approximation. Part 1: Tucker Case
SIAM Journal on Scientific Computing
Review of statistical network analysis: models, algorithms, and software
Statistical Analysis and Data Mining
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
Intruder or welcome friend: inferring group membership in online social networks
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Simmelian backbones: amplifying hidden homophily in Facebook networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Computers in Biology and Medicine
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We study the structure of social networks of students by examining the graphs of Facebook “friendships” at five U.S. universities at a single point in time. We investigate the community structure of each single-institution network and employ visual and quantitative tools, including standardized pair-counting methods, to measure the correlations between the network communities and a set of self-identified user characteristics (residence, class year, major, and high school). We review the basic properties and statistics of the employed pair-counting indices and recall, in simplified notation, a useful formula for the $z$-score of the Rand coefficient. Our study illustrates how to examine different instances of social networks constructed in similar environments, emphasizes the array of social forces that combine to form “communities,” and leads to comparative observations about online social structures, which reflect offline social structures. We calculate the relative contributions of different characteristics to the community structure of individual universities and compare these relative contributions at different universities. For example, we examine the importance of common high school affiliation at large state universities and the varying degrees of influence that common major can have on the social structure at different universities. The heterogeneity of the communities that we observe indicates that university networks typically have multiple organizing factors rather than a single dominant one.