Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 167-171.

• Data Science • Previous Articles     Next Articles

Dynamical Network Clustering Algorithm Based on Weighting Strategy

WANG Zi-jie1, ZHOU Ya-jing2, LI Hui-jia2   

  1. (Collaborative Innovation Center of Judicial Civilization,China University of Political Science and Law,Beijing 100080,China)1;
    (Central University of Finance and Economics,School of Management Science and Engineering,Beijing 100081,China)2
  • Online:2019-11-10 Published:2019-11-20

Abstract: Network dynamic plays an important role in analyzing the correlation between the function properties and the topological structure.This paper proposed a novel dynamical iteration algorithm incorporating the iterative process of membership vector with weighting scheme,i.e.weighting W and tightness T.These new elements can be used to adjust the link strength and the node compactness for improving the speed and accuracy of community structure detection.To estimate the optimal stop time of iteration,this paper utilized stability function defined as the Markov random walk auto-covariance.The algorithm is very efficient,and doesn’t need to specify the number of communities in advance,so it naturally supports overlapping communities by associating each node with a membership vector describing node’s involvement in each community.Theoretical analysis and experiments show that the algorithm can uncover communities effectively and efficiently.

Key words: Dynamical iteration algorithm, Network clustering, Weighting strategy, Tightness, Judicial case network

CLC Number: 

  • TP393
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