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NOCD: a new overlapping community detection algorithm based on improved KNN

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Abstract

In social networks, the community detection algorithm is very important for understanding the structures and the functions of these networks. A lot of researches have been done on the overlapping community detection algorithms as the overlapping is a significant feature of such networks. However, though many algorithms have been introduced to detect overlapping communities, the detection of the overlapping community is still a challenging task. In fact, the traditional static methods which partitioned the network structure could not efficiently obtain the latest community structure. The problems of high computational complexity and low identification accuracy need to be solved. To address these issues, in this paper, we propose a New Overlapping Community Detection algorithm based on improved KNN (called NOCD), which can timely adjust the community structure based on different network changes, and ultimately obtains the results of the community partitions with a high degree of Q module. To deal with the weighted social networks, NOCD adopts similarity instead of distance to evaluate the network. The experimental results show that the proposed NOCD algorithm compared with the COPRA, the CPM, the DeCom, the PLPA, and the AI-LPA algorithms can effectively improve the detection accuracy, the efficiency of parallel computing, and reduce the time complexity.

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Data availability statement

The LFR (Lancichinetti-Fortunato-Radicchi) model introduced in [36] is the most widely used synthetic benchmark for the comparison of community detection algorithms.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their comments which helped them in improving the quality of the paper. This paper is supported by the Key Scientific and Technological Research Projects in Henan Province (Grand No. 192102210125) and Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) (SKLNST-2020-2-01) .

Funding

The funding has been received from Key Scientific and Technological Research Projects in Henan Province with Grant no. 192102210125; Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) with Grant no. SKLNST-2020-2-01.

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Authors and Affiliations

  1. School of Computer Science and Technology, Zhoukou Normal University, Zhoukou, 466001, China

    Shi Dong

  2. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Mudar Sarem

  3. General Organization of Remote Sensing, 12586, Damascus, Syria

    Mudar Sarem

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  1. Shi Dong
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  2. Mudar Sarem
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Correspondence to Shi Dong.

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Dong, S., Sarem, M. NOCD: a new overlapping community detection algorithm based on improved KNN. J Ambient Intell Human Comput 13, 3053–3063 (2022). https://doi.org/10.1007/s12652-022-03774-4

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