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Overlapping community detection algorithm based on similarity of node relationship

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Abstract

Community discovery is a vital link in the research of social networks aiming at the shortcomings of the current local extension-based community discovery algorithm in local community discovery and extension. In this paper, we proposed a algorithm based on relationship similarity and local extension overlapping community detection (RSLO). First, use the node's relationship similarity strategy to find close seed communities. Then, according to the discovered seed community, the similarity between the neighboring nodes of the community and the community is calculated, and the nodes whose similarity meets the threshold are selected. After that, an adaptive optimization function is used to expand the community. Finally, the free nodes that have not been divided into the community are divided into communities, thereby achieving a more comprehensive community discovery. We conduct experiments on classic datasets and artificially generated networks. The results show that the RSLO algorithm can find accurate and objective community structures.

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Data availability

The dataset comes from the classic open dataset in the community discovery domain.

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Funding

This work was supported in part by the National Social Science Fund of China 18BGL266 and National Natural Science Foundation of China 41571401.

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Authors and Affiliations

  1. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, People’s Republic of China

    Hongtao Liu, Zhiqiang Li & Ning Wang

Authors
  1. Hongtao Liu
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  2. Zhiqiang Li
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  3. Ning Wang
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Contributions

HL received his Master's degree in Computer Application Technology from Chongqing China Southwest University in 2001, and his Doctor’s degree in artificial intelligence from Chongqing China Southwest University in 2004. His research interests include natural language processing, social networking, and swarm intelligence. He is now an associate professor in the School of Computer science and Technology, Chongqing University of Posts and Telecommunications.And ZL received a Bachelor of Engineering degree in 2019. He is currently pursuing the master’s degree with the Chongqing University of Posts and Telecommunications. His research interests include social networks and data mining.And NW received a Bachelor of Science degree in 2017. He received his Master's degree from Chongqing University of Posts and Telecommunications in 2021. His research interests include machine learning and social networks.

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Correspondence to Ning Wang.

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Liu, H., Li, Z. & Wang, N. Overlapping community detection algorithm based on similarity of node relationship. Soft Comput 27, 13689–13700 (2023). https://doi.org/10.1007/s00500-023-08067-2

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