Computer Science ›› 2016, Vol. 43 ›› Issue (Z6): 395-399, 412.doi: 10.11896/j.issn.1002-137X.2016.6A.094

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Near Linear Time Community Detection Algorithm Based on Dynamical Evolution

REN Luo-kun, LI Hui-jia and JIA Chuan-liang   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Detecting communities is is crucial for analyzing and designing complicated natural and engineering network.The existing community detection algorithms rely heavily on optimization and heuristic methods,which can not balance computational efficiency and accuracy simultaneously.Thus we proposed an evolutionary algorithm which uses a new dynamical system based on community membership vector to formulate the conditions driving the convergence of dynamics trajectory.Then,we proposed a quality function,which can unify the conventional algorithms by selecting appropriate parameters.Furthermore,considering the difficulty in choosing parameters,we established a graph generative model according to the network prior information,by which the optimum formalism of the quality function can be obtained automatically.Our algorithm is highly efficient and the computational complexity is nearly linear with the number of all nodes in a sparse network.Finally,extensive experiments were performed in both artificial and real networks,which reveal much useful information.

Key words: Community detection,Evolutionary computation,Dynamical iterative systems,Near linear time

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