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Improvement of Spectral Clustering Method in Social Network Community Detection

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Advances in Information and Communication Technology (ICTA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 847))

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Abstract

Nowadays, community discovery on a social network is an important research direction in the field of computer science. Social networks are usually represented in the form of graph data structures. Therefore, the discovery of community on social networks is mainly associated with the clustering problem on the graph. To solve the problem, there are many algorithms that are interested in research. In this paper, we will present an aggregate method, that is, clustering graphs based on the concept of spectrum to reduce the number of dimensions of the data set to be considered, thus reducing complexity. In addition, the modularity of the algorithm is focused on improving. Our algorithm is highly effective for large social networks. Computation quickly and resulting in community structure detection on social networks. Tests on a set of popular, standard social networks and certain real network have shown the high speed and high effiency in finding communities.

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Author information

Authors and Affiliations

  1. Thai Nguyen University of Information and Communication Technology, Thai Nguyen, Vietnam

    Nguyen Hien Trinh

  2. Thai Nguyen University of Education, Thai Nguyen, Vietnam

    Cap Thanh Tung

Authors
  1. Nguyen Hien Trinh
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  2. Cap Thanh Tung
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Corresponding author

Correspondence to Nguyen Hien Trinh .

Editor information

Editors and Affiliations

  1. Thai Nguyen University of Information and Communication Technology, Thai Nguyen, Vietnam

    Phung Trung Nghia

  2. Thai Nguyen University of Information and Communication Technology, Thai Nguyen, Vietnam

    Vu Duc Thai

  3. VNU University of Engineering and Technology, Vietnam National University, Ha Noi, Vietnam

    Nguyen Thanh Thuy

  4. Information Technology Institute, Vietnam National University, Ha Noi, Vietnam

    Le Hoang Son

  5. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Van-Nam Huynh

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Trinh, N.H., Tung, C.T. (2023). Improvement of Spectral Clustering Method in Social Network Community Detection. In: Nghia, P.T., Thai, V.D., Thuy, N.T., Son, L.H., Huynh, VN. (eds) Advances in Information and Communication Technology. ICTA 2023. Lecture Notes in Networks and Systems, vol 847. Springer, Cham. https://doi.org/10.1007/978-3-031-49529-8_29

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