Discovering Effective Communities in Social Networks Using Fuzzy Semantic Relations Elicitation

Document Type : Persian Original Article

Authors

1 Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran

2 Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar,,Iran

3 Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran

Abstract

Discovering Communities is a fundamental problem in understanding network performance for social network analysis. Traditional community detection methods merely consider the network topologies. Nonetheless, social media contains valuable data about people's interests, concerns, and sentiments, which is not reflected in the structure. The semantic solutions eventuate to the mislay of precious structural information. Most existing combinatorial methods favor one of the mentioned kinds and have limited performance. This paper introduces a 2-phase way based on fuzzy inferences to determine effective social network communities.  A series of real-life and synthetic networks have been used to evaluate the proposed method compared with several relevant algorithms. The experimental results proved that the proposed approach performs better in detecting meaningful communities and is more effective concerning network coherence and node attributes.

Keywords

References

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Journal of Soft Computing and Information Technology
Volume 12, Issue 3
December 2023
Pages 58-73

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