Multiple local community detection with integrated node attributes
Multiple local community detection is a key technology in social network analysis,aiming to reveal the multiple af-filiations and complex connections of users within networks.Addressing the issue that most existing multiple local community detection algorithms are based on network topology and neglect node attribute information,this paper introduced an algorithm named multiple local community detection with integrated node attributes(MLCDINA).This algorithm combined the structure and attribute information of the attributed network to determine the edge weights between node pairs and evaluated the impor-tance of the integration of structure and attributes(IISA)through random walks.In addition,the algorithm introduced a local clustering coefficient that considered edge weights and an intimacy random walk(IRW)to enhance the evaluation of subgraph density and IISA.Experimental results indicate that MLCDINA significantly improves the Jaccard F1-score over existing algo-rithms on real attributed networks,verifying its effectiveness in multiple local community detection tasks.
local community detectionattributed networkrandom walk
万方数据
维普
