English

High-Quality Disjoint and Overlapping Community Structure in Large-Scale Complex Networks

Social and Information Networks 2018-06-01 v1 Data Structures and Algorithms Physics and Society

Abstract

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.

Keywords

Cite

@article{arxiv.1805.12238,
  title  = {High-Quality Disjoint and Overlapping Community Structure in Large-Scale Complex Networks},
  author = {Eduar Castrillo and Elizabeth León and Jonatan Gómez},
  journal= {arXiv preprint arXiv:1805.12238},
  year   = {2018}
}

Comments

8 pages, 5 figures, 3 tables, sent to peer-review to the International Symposium on Foundations and Applications of Big Data Analytics FAB 2018

R2 v1 2026-06-23T02:14:04.585Z