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 ZHANG Meiqin,BAI Liang,WANG Junbin.Label propagation algorithm based on weighted clustering ensemble[J].CAAI Transactions on Intelligent Systems,2018,13(06):994-998.[doi:10.11992/tis.201806011]





Label propagation algorithm based on weighted clustering ensemble
张美琴1 白亮2 王俊斌1
1. 山西大学 计算机与信息技术学院, 山西 太原 030006;
2. 山西大学 计算智能与中文信息处理教育部重点实验室, 山西 太原 030006
ZHANG Meiqin1 BAI Liang2 WANG Junbin1
1. College of Computer Science and Technology, Shanxi University, Taiyuan 030006, China;
2. Key Laboratory of Symbol Computation and Knowledge Engineering(Shanxi University), Ministry of Education, Taiyuan 030006, China
data miningnetwork datacommunity detectionlabel propagation algorithmclustering ensemblebase clusteringmodularity measureweighted measure
Label propagation algorithm (LPA) is one of the high-efficiency community detection algorithms for processing large-scale network data. It has attracted much attention because of its nearly linear time complexity with the number of nodes. However, in the algorithm, the label of each node depends on the labels of its neighbor nodes, which makes the iteration speed and clustering performance of the algorithm very sensitive to the order of label information update; this influences the accuracy and stability of the community detection result. To solve this problem, a new LPA is proposed based on weighted clustering ensemble. The new algorithm runs the LPAs many times to obtain several partition results, which can be regarded as a base clustering set. Furthermore, the modularity measure is used to evaluate the importance of each clustering. Based on the evaluation results, a weighted similarity measure is defined between nodes to obtain a weighted similarity matrix of pairwise nodes. Finally, hierarchical clustering on the similarity matrix is used to obtain a final community division result. In the experimental analysis, the new algorithm is compared with several other improved LPAs on five real representative network datasets. The experimental results show that the new algorithm is more effective for improving the community detection accuracy.


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更新日期/Last Update: 2018-12-25