Mod-Müllner: an efficient algorithm for hierarchical community analysis in large networks
by Brieuc Conan-Guez; Manh Cuong Nguyen
International Journal of Social Network Mining (IJSNM), Vol. 2, No. 2, 2015

Abstract: In this work, we propose a new efficient agglomerative algorithm for hierarchical clustering analysis (HCA) of large networks. This algorithm, called Mod-Müllner, is an adaptation of an existing algorithm proposed by Müllner in 2011 and initially dedicated to HCA of pairwise dissimilarities. Mod-Müllner performs a greedy optimisation of the modularity, a widely used measure for network partitioning. We show that thanks to adapted data structures, Mod-Müllner achieves lower running times than other agglomerative algorithms, while producing comparable solutions. Finally, Mod-Müllner is compared to the Louvain method on simulated and real-world datasets. For both methods, the improved solutions obtained through single-level and multilevel refinements are studied.

Online publication date: Thu, 08-Oct-2015

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