A community detection method integrating clustering coefficient and node similarity optimization
TIAN Le
CAO Langcai
Abstract：Non-negative matrix factorization is widely used in the field of community detection due to its effectiveness and easy interpretation.Most existing methods only consider network topology information and ignore node similarity information.To address this problem,a community detection model based on symmetric non-negative matrix factorization that integrates clustering information and similarity is proposed.The algorithm uses a variety of indicators to describe the node similarity of the network,and further integrates the clustering coefficient to optimize the node similarity,so that it can play a better role and improve the accuracy of community discovery.This paper conducts experiments on four real-world network datasets,and compares the node similarity before and after optimization based on the evaluation indicators.The experimental results show that the optimization model in this paper can effectively improve the accuracy of network prediction.
Keywords：community detectiontopologynode similaritynon-negative matrix factorizationclustering coefficient
Publication Date：2025-11-30
Online Publishing Date：2025-12-12(First online date of this platform, not the publication date of the document)
Pages：7( 1-7 )