Overview on Evaluation Indicators for Overlapping Community Discovery Algorithms
Overlapping community discovery has a strong application value for understanding complex systems and discovering hidden laws in complex networks.Evaluation indicator is a key factor to find high-quality overlapping communities,and the progress of algorithms often depends on the progress of evaluation indicator.Existing studies have summarized the evaluation indicator of non-over-lapping community discovery algorithms,but have not summarized the evaluation indicator of overlapping community discovery algorithms.The evaluation indicators of overlapping community discovery algorithms are systematically summarized and reviewed.The indicators are divided into three categories:community structure known in advance,community structure unknown in advance and others.The evaluation indicators that the community structure is known in advance include three subcategories:confusion matrix-based,ARI-based and NMI-based.The evaluation indicators that the community structure is unknown in advance include three subcategories:modularity-based,density-based and metadata-based.The other categories mainly introduce the scalability indicator for large networks of millions of nodes and edges.A thorough understanding of various evaluation indicators is of great value to the development and opti-mization of overlapping community discovery algorithms and the discovery of high-quality communities in practical applications.
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