A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Local Graph Partitioning using PageRank Vectors
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
The life and death of online groups: predicting group growth and longevity
Proceedings of the fifth ACM international conference on Web search and data mining
Computer Science Review
Distinguishing topical and social groups based on common identity and bond theory
Proceedings of the sixth ACM international conference on Web search and data mining
Organizational overlap on social networks and its applications
Proceedings of the 22nd international conference on World Wide Web
Computer science fields as ground-truth communities: their impact, rise and fall
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
On fast parallel detection of strongly connected components (SCC) in small-world graphs
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Deep Twitter diving: exploring topical groups in microblogs at scale
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
Minimizing data transfers for regular reachability queries on distributed graphs
Proceedings of the Fourth Symposium on Information and Communication Technology
Random walks based modularity: application to semi-supervised learning
Proceedings of the 23rd international conference on World wide web
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Nodes in real-world networks, such as social, information or technological networks, organize into communities where edges appear with high concentration among the members of the community. Identifying communities in networks has proven to be a challenging task mainly due to a plethora of definitions of a community, intractability of algorithms, issues with evaluation and the lack of a reliable gold-standard ground-truth. We study a set of 230 large social, collaboration and information networks where nodes explicitly define group memberships. We use these groups to define the notion of ground-truth communities. We then propose a methodology which allows us to compare and quantitatively evaluate different definitions of network communities on a large scale. We choose 13 commonly used definitions of network communities and examine their quality, sensitivity and robustness. We show that the 13 definitions naturally group into four classes. We find that two of these definitions, Conductance and Triad-participation-ratio, consistently give the best performance in identifying ground-truth communities.