Generalized Louvain method for community detection in large networks

By Pasquale De Meo, Emilio Ferrara, Giacomo Fiumara, Alessandro Provetti. International Conference on Intelligent Systems Design and Applications, 2011

417 citations recorded by the source corpus.

In this paper we present a novel strategy to discover the community structure of (possibly, large) networks. This approach is based on the well-know concept of network modularity optimization. To do so, our algorithm exploits a novel measure of edge centrality, based on the κ-paths. This technique allows to efficiently compute a edge ranking in large networks in near linear time. Once the centrality ranking is calculated, the algorithm computes the pairwise proximity between nodes of the network. Finally, it discovers the community structure adopting a strategy inspired by the well-known state-of-the-art Louvain method (henceforth, LM), efficiently maximizing the network modularity. The experiments we carried out show that our algorithm outperforms other techniques and slightly improves results of the original LM, providing reliable results. Another advantage is that its adoption is naturally extended even to unweighted networks, differently with respect to the LM.

Subfields: Geometry and Topology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/85701336890b11947d0c97f18c5682d8112fd17e

Citations in the corpus

• "Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models (2023, neutral): “5 and Louvain algorithm [47] as our community detection algorithm (see Section A.”