[20] J. Leskovec, J. M. Kleinberg, and C. Faloutsos. Graph evolution: Densification and shrinking diameters. ACM Trans. Knowl. Discov. Data, 1(1):2, 2007. doi: 10.1145/1217299.1217301.
[21] J. MacQueen. Classification and analysis of multivariate observations. In 5th Berkeley Symp. Math. Statist. Probability, pages 281-297. University of California Los Angeles LA USA, 1967.
[22] T. Mansour, M. Schork, and M. Shattuck. The generalized Stirling and Bell numbers revisited. J. Integer Seq, 15(8):47, 2012.
[23] E. Müller, S. Gunnemann, I. Färber, and T. Seidl. Discovering multiple clustering solutions: Grouping objects in different views of the data. In G. I. Webb, B. Liu, C. Zhang, D. Gunopoulos, and X. Wu, editors, ICDM 2010, the 10th IEEE International Conference on Data Mining, Sydney, Australia, 14-17 December 2010, page 1220. IEEE Computer Society, 2010. doi: 10.1109/ICDM.2010.85.
[24] X. V. Nguyen, J. Epps, and J. Bailey. Information theoretic measures for clusterings comparison: Is a correction for chance necessary? In A. P. Danyluk, L. Bottou, and M. L. Littman, editors, Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009, volume 382 of ACM International Conference Proceeding Series, pages 1073-1080. ACM, 2009. doi: 10.1145/1553374.1553511.
[25] X. V. Nguyen, J. Epps, and J. Bailey. Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. Journal of Machine Learning Research, 11:2837-2854, 2010.
[26] W. M. Patefield. Algorithm AS 159: An efficient method of generating random  $\mathbf{R} \times \mathbf{C}$  tables with given row and column totals. Journal of the Royal Statistical Society. Series C (Applied Statistics), 30(1):91-97, 1981. ISSN 00359254, 14679876.
[27] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournaopeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.
[28] U. N. Raghavan, R. Albert, and S. Kumara. Near linear time algorithm to detect community structures in large-scale networks. Physical review E, 76(3):036106, 2007.
[29] S. Romano, J. Bailey, X. V. Nguyen, and K. Verspoor. Standardized mutual information for clustering comparisons: One step further in adjustment for chance. In Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014, volume 32 of JMLR Workshop and Conference Proceedings, pages 1143-1151, 2014.
[30] S. Romano, X. V. Nguyen, J. Bailey, and K. Verspoor. Adjusting for chance clustering comparison measures. Journal of Machine Learning Research, 17:134:1-134:32, 2016.
[31] D. A. Simovici. On generalized entropy and entropic metrics. J. Multiple Valued Log. Soft Comput., 13(4-6):295-320, 2007.
[32] C. L. Staudt, A. Sazonovs, and H. Meyerhenke. NetworkKit: A tool suite for large-scale complex network analysis. Network Science, 4(4):508-530, 2016. doi: 10.1017/nws.2016.20.
[33] X. Su, S. Xue, F. Liu, J. Wu, J. Yang, C. Zhou, W. Hu, C. Paris, S. Nepal, D. Jin, Q. Z. Sheng, and P. S. Yu. A comprehensive survey on community detection with deep learning. CoRR, abs/2105.12584, 2021.
[34] V. A. Traag, L. Waltman, and N. J. Van Eck. From Louvain to Leiden: Guaranteeing well-connected communities. Scientific reports, 9(1):5233, 2019.
[35] C. Tsallis. Possible generalization of Boltzmann-Gibbs statistics. Journal of Statistical Physics, 52(1):479-487, July 1988. ISSN 1572-9613. doi: 10.1007/BF01016429.
[36] J. Vanschoren, J. N. van Rijn, B. Bischl, and L. Torgo. Openml: networked science in machine learning. SIGKDD Explorations, 15(2):49-60, 2013. doi: 10.1145/2641190.2641198.