Discovery of Web user communities and their role in personalization
User Modeling and User-Adapted Interaction
Review of statistical network analysis: models, algorithms, and software
Statistical Analysis and Data Mining
Face-to-face contacts at a conference: dynamics of communities and roles
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
OCTracker: A Density-Based Framework for Tracking the Evolution of Overlapping Communities in OSNs
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Characterising and modelling social networks with overlapping communities
International Journal of Web Based Communities
ChurnVis: visualizing mobile telecommunications churn on a social network with attributes
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Spectral graph multisection through orthogonality
Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering
Overlapping community detection in networks: The state-of-the-art and comparative study
ACM Computing Surveys (CSUR)
A link clustering based overlapping community detection algorithm
Data & Knowledge Engineering
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As research into community finding in social networks progresses, there is a need for algorithms capable of detecting overlapping community structure. Many algorithms have been proposed in recent years that are capable of assigning each node to more than a single community. The performance of these algorithms tends to degrade when the ground-truth contains a more highly overlapping community structure, with nodes assigned to more than two communities. Such highly overlapping structure is likely to exist in many social networks, such as Facebook friendship networks. In this paper we present a scalable algorithm, MOSES, based on a statistical model of community structure, which is capable of detecting highly overlapping community structure, especially when there is variance in the number of communities each node is in. In evaluation on synthetic data MOSES is found to be superior to existing algorithms, especially at high levels of overlap. We demonstrate MOSES on real social network data by analyzing the networks of friendship links between students of five US universities.