An Extended Louvain Method for Community Detection in Attributed Social Networks

Subject Areas : journal of Artificial Intelligence in Electrical Engineering

Yasser Sadri ¹ , Saeid Taghavi Afshord ² , shahriar lotfi ³ , Vahid Majidnezhad ⁴

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.
4 - Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran

Received: 2022-09-10 Accepted : 2022-11-20 Published : 2022-12-01

Keywords: semantics, Community Detection, Social Networks Analysis, Complex networks, Extended Louvain,

Abstract :

Community detection is a significant way to analyze complex networks. Classical methods usually deal only with the network's structure and ignore content features. During the last decade, most solutions for community detection only consider network topology. Social networks, as complex systems, contain actors with certain social connections. Moreover, most real-world social networks provide additional data about actors, such as age, gender, preferences, etc. However, content-based methods lead to the loss of valuable topology information. This paper describes and clarifies the problems and proposes a fast and deterministic method for discovering communities in social networks to combine structure and semantics. The proposed method has been evaluated through simulation experiments, showing efficient performance in network topology and semantic criteria and achieving proportional performance for community detection.