A robust ant colony optimization-based algorithm for community mining in large scale oriented social graphs

• Published in: Expert systems with applications Vol. 40; no. 14; pp. 5709 - 5718
• Main Authors: Ben Romdhane, L., Chaabani, Y., Zardi, H.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 15.10.2013; Elsevier
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Ant colony optimization; Applied sciences; Biological and medical sciences; Communities; Community detection; Computer science; control theory; systems; Computer simulation; Computer systems and distributed systems. User interface; Data processing. List processing. Character string processing; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; Graphs; Interactions. Associations; Intermolecular phenomena; Mathematical models; Memory organisation. Data processing; Molecular biophysics; Networks; NP-complete; Social networks; Software; Theoretical computing; Community detection; Ant colony optimization; Social networks; NP-complete; Network structure; Artificial life; Complex system; Molecular interaction; Data mining; Data flow processing; Efficiency; Sociology; Swarm intelligence; Robustness; Power law; Edge(graph); Network flow; Social network; Graph theory; Orientation; Weighted graph; Protein; Biological system; Asymmetry; Complex network; NP complete problem; Objective function; Molecular biology
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2013.04.021

•We propose a new objective function.•We define a new method for optimizing the objective function.•We tested our method in artificial and real networks. Community detection plays a key role in such important fields as biology, sociology and computer science. For example, detecting the communities in protein–protein interactions networks helps in understanding their functionalities. Most existing approaches were devoted to community mining in undirected social networks (either weighted or not). In fact, despite their ubiquity, few proposals were interested in community detection in oriented social networks. For example, in a friendship network, the influence between individuals could be asymmetric; in a networked environment, the flow of information could be unidirectional. In this paper, we propose an algorithm, called ACODIG, for community detection in oriented social networks. ACODIG uses an objective function based on measures of density and purity and incorporates the information about edge orientations in the social graph. ACODIG uses ant colony for its optimization. Simulation results on real-world as well as power law artificial benchmark networks reveal a good robustness of ACODIG and an efficiency in computing the real structure of the network.