A new single-chromosome evolutionary algorithm for community detection in complex networks by combining content and structural information. (30th December 2021)

• Record Type: Journal Article
• Authors: Pourabbasi, Elmira; Majidnezhad, Vahid; Taghavi Afshord, Saeid; Jafari, Yasser
• Is Part Of: Expert systems with applications. Volume 186(2021)
• Publication Date: 2021-12-30
• Subjects: Complex networks -- Community detection -- Combination of content and structural information -- New single-chromosome evolutionary algorithm -- Architectural modification operator; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115854
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Community detection through combination of content and structural information. A single-chromosome evolutionary algorithm with architectural modification operator. Used a new criterion, named CS, for measuring the content similarity of nodes. Abstract: Community detection is an important step in perceiving network structure and performance for complex network analysis. The rapid growth of network data in recent years has piqued the interest of many researchers in community detection. The majority of community detection methods only consider the network structure. Nonetheless, real-world network nodes may have some characteristics that can be useful for community detection. This study proposed a novel single-chromosome evolutionary algorithm with a distinctive architecture modification operator for community detection in complex networks using a combination of structural and content information. To this end, a novel virtual network was created by taking into account the structure and content of nodes, and communities were discovered for this network by optimizing the objective function (and using the combinatorial adjacency matrix instead of the structural adjacency matrix) in a series of steps. The nodes in this network were the same as the nodes in the main network; however, the links were developed based on similarities between nodes and their structural neighborhood. The proposed algorithm also included a method for sorting new nodes in order to determine the analysis order of nodes along with the local improvement of solution, as well as a new criterion, CS, for measuring the content similarity of nodes. The proposed algorithm was evaluated in real-networks and compared to various state-of-the-art and widely used methods. The Friedman rank algorithm was then used to rank the proposed algorithm and the existing methods using six real networks. According to the NMI criterion used in the Friedman rank test, the rank of the proposed algorithms increased by 96.8762%, 70.2693%, 26.0005%, 23.5294%, 46.5109%, and 23.5294% compared respectively with ASCD-ARC, BTLSC, Adapt-SA, PSB-PG, RSECD, and NEMBP, which have all been proposed in recent years.