A novel dynamical community detection algorithm based on weighting scheme

Ju Li (), Kai Yu () and Ke Hu ()
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Ju Li: School of Computer Science and Engineering, Changshu Institute of Technology, Changshu, Jiangsu 215500, P. R. China;
Kai Yu: School of Computer Science and Engineering, Xinjiang University of Finance & Economics, Urumqi, Xinjiang 830012, P. R. China
Ke Hu: Department of Physics, Xiangtan University, Xiangtan, Hunan 411105, P. R. China

International Journal of Modern Physics C (IJMPC), 2015, vol. 26, issue 08, 1-17

Abstract: Network dynamics plays an important role in analyzing the correlation between the function properties and the topological structure. In this paper, we propose a novel dynamical iteration (DI) algorithm, which incorporates the iterative process of membership vector with weighting scheme, i.e. weightingWand tightnessT. 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, we utilize a new stability measure which is defined as the Markov random walk auto-covariance. We do not need to specify the number of communities in advance. It naturally supports the overlapping communities by associating each node with a membership vector describing the node's involvement in each community. Theoretical analysis and experiments show that the algorithm can uncover communities effectively and efficiently.

Keywords: Community structure; weighting scheme; tightness; dynamical iteration; stability optimization; 89.75.Hc; 89.75.Fb (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1142/S0129183115500916

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