Computer Science ›› 2017, Vol. 44 ›› Issue (Z6): 433-437.doi: 10.11896/j.issn.1002-137X.2017.6A.097

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Attributed Graph Clustering Algorithm Based on Cluster-aware Multiagent System

SHI Kai, REN Luo-kun, PENG Yi-ming and LI Hui-jia   

  • Online:2017-12-01 Published:2018-12-01

Abstract: Existing methods to partition nodes into clusters with tight correlations is to apply clustering techniques on attributed graphs based on node connectivity or attribute similarity.In this paper,we comprehend each node as an autonomous agent and developed an accurate multi-agent system to extract overlapping clusters in attributed graphs.First,a kernel function with bandwidth factor is introduced to measure the influence of each agent,and those agents with highest local influence are selected as leader agents.Next,a novel local expansion strategy is proposed,by which each leader agent absorbs the closest followers in the graph.Then,the cluster-aware multiagent system was designed so that the optimal overlapping cluster configuration can be uncovered.Our method is highly efficient,whose computational time nearly linearly depends on the number of edges.Finally,the proposed method is demonstrated on synthetic benchmark graphs and real-life attributed graphs to verify the systematic performance.

Key words: Clustering,Attributed graph,Multiagent cluter-aware system,Centrality,Overlapping nodes

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