Multiobjective approach for detecting communities in heterogeneous networks. (9th August 2017)

• Record Type: Journal Article
• Authors: Karimi‐Majd, Amir‐Mohsen; Fathian, Mohammad
• Is Part Of: Computational intelligence. Volume 33:Number 4(2017)
• Page Start: 980
• Page End: 1004
• Publication Date: 2017-08-09
• Subjects: community detection -- heterogeneous network -- knowledge mining -- metaheuristic algorithm -- multiobjective optimization; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12137
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Online social networks have a strong potential to be divided into a number of dense substructures, called communities. In such heterogeneous networks, the communities refer not only to dense parts of links but also to clusters present among other dimensions such as users' profiles, comments, and information flows. To find communities in these networks, researchers have developed a number of methods; however, to the best of the authors' knowledge, these methods are limited in taking only 2 dimensions into account, and they are also not able to give a sense of how users behave in their communities. To deal with these issues, this paper proposes a multiobjective optimization model in which a specific objective function has been used for each considered dimension in a given network. Because of the NP‐hardness of the studied problem, an efficient and effective multiobjective metaheuristic algorithm has been developed. By juxtaposing the nondominated solutions obtained, the proposed algorithm can demonstrate how users behave in their communities. To illustrate the effectiveness of the algorithm, a set of experiments with a comprehensive evaluation method is provided. The results show the superiority and the stability of the proposed algorithm.