Abstract
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.
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