2021/08/30 by Niko Motschnig, Alexander Ramharter, Motschnig, Niko +7
Computer Science · Physics and Astronomy · #Advanced Clustering Algorithms Research #Complex Network Analysis Techniques #FOS: Computer and information sciences #Opinion Dynamics and Social Influence #Social and Information Networks (cs.SI) #cs.SI
paper · pdf · doi:10.48550/arxiv.2108.13482
arxiv created 2021/08/30 · openalex publication_date 2021/08/30 · arxiv updated 2021/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work, we explore four common algorithms for community detection in networks, namely Agglomerative Hierarchical Clustering, Divisive Hierarchical Clustering (Girvan-Newman), Fastgreedy and the Louvain Method. We investigate their mechanics and compare their differences in terms of implementation and results of the clustering behavior on a standard dataset. We further propose some enhancements to these algorithms that show promising results in our evaluations, such as self-neighboring for Neighbor Matrix constructions, a deterministic slightly faster version of the Louvain Method that favors less bigger clusters and various implementation changes to the Fastgreedy algorithm.