A Fast Algorithm to Find Overlapping Communities in Networks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Hierarchical, Parameter-Free Community Discovery
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
Combining link and content for community detection: a discriminative approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Parallel community detection on large networks with propinquity dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
Fast detection of size-constrained communities in large networks
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
The SemSets model for ad-hoc semantic list search
Proceedings of the 21st international conference on World Wide Web
On the separability of structural classes of communities
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Defining and Evaluating Network Communities Based on Ground-Truth
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
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Graph clustering, often addressed as community detection, is a prominent task in the domain of graph data mining with dozens of algorithms proposed in recent years. In this paper, we focus on several popular community detection algorithms with low computational complexity and with decent performance on the artificial benchmarks, and we study their behaviour on real-world networks. Motivated by the observation that there is a class of networks for which the community detection methods fail to deliver good community structure, we examine the assortativity coefficient of ground-truth communities and show that assortativity of a community structure can be very different from the assortativity of the original network. We then examine the possibility of exploiting the latter by weighting edges of a network with the aim to improve the community detection outputs for networks with assortative community structure. The evaluation shows that the proposed weighting can significantly improve the results of community detection methods on networks with assortative community structure.