[1] Tyler, J.R., Wilkinson, D.M.: Automated discovery of community structure within organization. Phys. Rev. E 15, 723–739 (2003)

[2] Gregory, S.: An algorithm to find overlapping community structure innetworks. Knowledge Discovery in Databases: PKDD. pp. 91−102 (2007).

[3] Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25(2), 163–177 (2001)

[4] Von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), arXiv:0711.0189 [cs.Ds] (2007)

[5] Tang, F., Wang, Y., Su, J., Wang, C.: Spectral clustering-based network community detection with node attributes. Stat. Interface 12, 123–133 (2019)

[6] 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)

[7] 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)

[8] Richard, L., Burden, J.: Douglas Faires: Numerical Analysis, 9th edn. Brooks/Cole (2011)

[9] Leskovec, J., Krevl, A.: SNAP datasets tanford large network dataset collection. https://snap.stanford.edu (2014)