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) #cs.DS #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1805.12238
8 pages, 5 figures, 3 tables, sent to peer-review to the International Symposium on Foundations and Applications of Big Data Analytics FAB 2018
arxiv created 2018/05/30 · openalex publication_date 2018/05/30 · arxiv updated 2018/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we propose an improved version of an agglomerative hierarchical clustering algorithm that performs disjoint community detection in large-scale complex networks. The improved algorithm is achieved after replacing the local structural similarity used in the original algorithm, with the recently proposed Dynamic Structural Similarity. Additionally, the improved algorithm is extended to detect fuzzy and crisp overlapping community structure. The extended algorithm leverages the disjoint community structure generated by itself and the dynamic structural similarity measures, to compute a proposed membership probability function that defines the fuzzy communities. Moreover, an experimental evaluation is performed on reference benchmark graphs in order to compare the proposed algorithms with the state-of-the-art.