2018/05/30 by Eduar Castrillo, Castrillo, Eduar, Elizabeth León +3
Computer Science · Physics and Astronomy · #Advanced Clustering Algorithms Research #Advanced Graph Neural Networks #Complex Network Analysis Techniques #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Physical sciences #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1805.12238
openalex publication_date 2018/05/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we propose an improved version of an agglomerative\nhierarchical clustering algorithm that performs disjoint community detection in\nlarge-scale complex networks. The improved algorithm is achieved after\nreplacing the local structural similarity used in the original algorithm, with\nthe recently proposed Dynamic Structural Similarity. Additionally, the improved\nalgorithm is extended to detect fuzzy and crisp overlapping community\nstructure. The extended algorithm leverages the disjoint community structure\ngenerated by itself and the dynamic structural similarity measures, to compute\na proposed membership probability function that defines the fuzzy communities.\nMoreover, an experimental evaluation is performed on reference benchmark graphs\nin order to compare the proposed algorithms with the state-of-the-art.\n