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TSCDA: a dynamic two-stage community discovery approach

TSCDA: a dynamic two-stage community discovery approach In this paper, we introduce a new approach for detecting community structures in networks. The approach is subject to modifying one of the connectivity-based community quality functions based on considering the impact that each community’s most influential node has on the other vertices. Utilizing the proposed quality measure, we devise an algorithm that aims to detect high-quality communities of a given network based on two stages: finding a promising initial solution using a greedy method and then refining the solutions in a local search manner. The algorithm’s performance has been evaluated on various standard real-world networks and artificial graphs. The quality of the results has been reported and compared with those obtained by several state-of-the-art algorithms. As it turns out, the proposed approach is competitive with the other well-known techniques in the literature and significantly outperforms them. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Social Network Analysis and Mining Springer Journals

TSCDA: a dynamic two-stage community discovery approach

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022
ISSN
1869-5450
eISSN
1869-5469
DOI
10.1007/s13278-022-00874-z
Publisher site
See Article on Publisher Site

Abstract

In this paper, we introduce a new approach for detecting community structures in networks. The approach is subject to modifying one of the connectivity-based community quality functions based on considering the impact that each community’s most influential node has on the other vertices. Utilizing the proposed quality measure, we devise an algorithm that aims to detect high-quality communities of a given network based on two stages: finding a promising initial solution using a greedy method and then refining the solutions in a local search manner. The algorithm’s performance has been evaluated on various standard real-world networks and artificial graphs. The quality of the results has been reported and compared with those obtained by several state-of-the-art algorithms. As it turns out, the proposed approach is competitive with the other well-known techniques in the literature and significantly outperforms them.

Journal

Social Network Analysis and MiningSpringer Journals

Published: Dec 1, 2022

Keywords: Graph partitioning; Community detection; Heuristic approach; Local search

References