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True-link clustering through signaling process and subcommunity merge in overlapping community detection

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Abstract

To overcome the difficulty in detecting reliable overlapping communities in complex networks, “true-link” and “pseudo-link” are firstly proposed on the basis of the original network graph. Then, the “true-link” graph is obtained through the preprocessing of the original network graph. And then the line graph is partitioned by means of signaling process and single-linkage hierarchical clustering. Meanwhile, the subcommunities are merged based on the proposed similarity between communities, which eradicates the inherently redundant overlapping communities to a certain extent. Compared with other overlapping community detection algorithms, this proposed algorithm is of strong robustness and high accuracy. All the results of the experiments boil down to the conclusion that this True-link Clustering Community Detection is an overlapping community detection algorithm prevailing over others.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (51674113), Hunan Provincial Natural Science Foundation of China (2017JJ4003) and (14JJ4026).

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Authors and Affiliations

  1. College of Information Science and Engineering, HNU, Changsha, China

    Yingjie Zhang, Ying Zhang, Qiankun Chen & Zhonghan Gong

  2. Institute of Language and Cognition, HNU, Changsha, China

    Zhaoyang Ai

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  1. Yingjie Zhang
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  2. Ying Zhang
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Correspondence to Yingjie Zhang.

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Zhang, Y., Zhang, Y., Chen, Q. et al. True-link clustering through signaling process and subcommunity merge in overlapping community detection. Neural Comput & Applic 30, 3613–3621 (2018). https://doi.org/10.1007/s00521-017-2946-3

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