Community detection refers to extracting dense interacting nodes or subgraphs that form relevant aggregation (aka, communities) within networks. We present nine community detection methods based on different approaches, and we compare them on the Girvan-Newman community detection benchmark network. Two methods proposed by our group using spectral graph theory and fuzzy clustering obtain the best experimental results evaluated using the Omega Index.

Comparison of methods for community detection in networks

MASULLI, FRANCESCO;ROVETTA, STEFANO;ABDULLATIF, AMR RASHAD AHMED
2016

Abstract

Community detection refers to extracting dense interacting nodes or subgraphs that form relevant aggregation (aka, communities) within networks. We present nine community detection methods based on different approaches, and we compare them on the Girvan-Newman community detection benchmark network. Two methods proposed by our group using spectral graph theory and fuzzy clustering obtain the best experimental results evaluated using the Omega Index.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/858454
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