A Relationships-based Algorithm for Detecting the Communities in Social Networks

Document Type : Original Article


Department of Computer Science, University of Tabriz, Tabriz, Iran


Social network research analyzes the relationships between interactions, people, organizations, and entities. With the developing reputation of social media, community detection is drawing the attention of researchers. The purpose of community detection is to divide social networks into groups. These communities are made of entities that are very closely related. Communities are defined as groups of nodes or summits that have strong relationships among themselves rather than between themselves. The clustering of social networks is important for revealing the basic structures of social networks and discovering the hyperlink of systems on human beings and their interactions. Social networks can be represented by graphs where users are shown with the nodes of the graph and the relationships between the users are shown with the edges. Communities are detected through clustering algorithms. In this paper, we proposed a new clustering algorithm that takes into account the extent of relationships among people. Outcomes from particular data suggest that taking into account the profundity of people-to-people relationships increases the correctness of the aggregation methods.


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Habib Izadkhah is an Associate Professor at the Department of Computer Science, University of Tabriz, Iran. His research interests include graph theory, optimization algorithms, software engineering, and deep learning. He contributed to various research projects, co-authored a number of research papers in international conferences, workshops and journals. He has authored a book entitled Deep Learning in Bioinformatics, published by Elsevier.

Javad Hajipour is currently an assistant professor at the University of Tabriz, Iran. He received a Ph.D. degree in electronic and computer engineering from the University of British Columbia, Canada. He completed his BSc at Sharif University of Technology, Iran; and received MSc degree from Iran University of Science and Technology (IUST).

Sevda Fotovvat completed her BSc and MSc education in computer science at University of Tabriz, Iran.