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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tyler, J.R., Wilkinson, D.M.: Automated discovery of community structure within organization. Phys. Rev. E 15, 723–739 (2003)
Gregory, S.: An algorithm to find overlapping community structure innetworks. Knowledge Discovery in Databases: PKDD. pp. 91−102 (2007).
Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25(2), 163–177 (2001)
Von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), arXiv:0711.0189 [cs.Ds] (2007)
Tang, F., Wang, Y., Su, J., Wang, C.: Spectral clustering-based network community detection with node attributes. Stat. Interface 12, 123–133 (2019)
Ren, S., Zhang, S., Wu, T.: An improved spectral clustering community detection algorithm based on probability matrix. Discret. Dyn. Nat. Soc. 2020, 6. Article ID 4540302. https://doi.org/10.1155/2020/4540302 (2020)
Ding, S., Jia, H., Du, M., Xue, Y.: A semi-supervised approximate spectral clustering algorithm based on HMRF model. Inf. Sci. 429, 215–228 (2018)
Richard, L., Burden, J.: Douglas Faires: Numerical Analysis, 9th edn. Brooks/Cole (2011)
Leskovec, J., Krevl, A.: SNAP datasets tanford large network dataset collection. https://snap.stanford.edu (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-49529-8_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49528-1
Online ISBN: 978-3-031-49529-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)