Finding community structure in mega-scale social networks: [extended abstract]
Proceedings of the 16th international conference on World Wide Web
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
An Algorithm to Find Overlapping Community Structure in Networks
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ICML'06 Proceedings of the 2006 conference on Statistical network analysis
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ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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FRINGE: a new approach to the detection of overlapping communities in graphs
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A novel genetic algorithm for overlapping community detection
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Community detection in social networks through community formation games
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Using the omega index for evaluating abstractive community detection
Proceedings of Workshop on Evaluation Metrics and System Comparison for Automatic Summarization
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Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
A new overlapping clustering algorithm based on graph theory
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
On community detection in real-world networks and the importance of degree assortativity
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Overlapping community detection in networks: The state-of-the-art and comparative study
ACM Computing Surveys (CSUR)
A separability framework for analyzing community structure
ACM Transactions on Knowledge Discovery from Data (TKDD) - Casin special issue
A link clustering based overlapping community detection algorithm
Data & Knowledge Engineering
RoClust: Role discovery for graph clustering
Web Intelligence and Agent Systems
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Many networks possess a community structure, such that vertices form densely connected groups which are more sparsely linked to other groups. In some cases these groups overlap, with some vertices shared between two or more communities. Discovering communities in networks is a computationally challenging task, especially if they overlap. In previous work we proposed an algorithm, CONGA, that could detect overlapping communities using the new concept of split betweenness. Here we present an improved algorithm based on a localform of betweenness, which yields good results but is much faster. It is especially effective in discovering small-diameter communities in large networks, and has a time complexity of only O(nlog n) for sparse networks.