On clusterings: Good, bad and spectral
Journal of the ACM (JACM)
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Graph Visualization Techniques for Web Clustering Engines
IEEE Transactions on Visualization and Computer Graphics
Microscopic evolution of social networks
Proceedings of the 14th 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
Social based layouts for the increase of locality in graph operations
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
High quality, scalable and parallel community detection for large real graphs
Proceedings of the 23rd international conference on World wide web
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Community detection has arisen as one of the most relevant topics in the field of graph data mining due to its importance in many fields such as biology, social networks or network traffic analysis. The metrics proposed to shape communities are too lax and do not consider the internal layout of the edges in the community, which lead to undesirable results. We define a new community metric called WCC. The proposed metric meets a minimum set of basic properties that guarantees communities with structure and cohesion. We experimentally show that WCC correctly quantifies the quality of communities and community partitions using real and synthetic datasets, and compare some of the most used community detection algorithms in the state of the art.