TY - JOUR ID - 164089 TI - A Relationships-based Algorithm for Detecting the Communities in Social Networks JO - International Journal of Web Research JA - IJWR LA - en SN - 2645-4335 AU - Fotovvat, Sevda AU - Izadkhah, Habib AU - Hajipour, Javad AD - Department of Computer Science, University of Tabriz, Tabriz, Iran Y1 - 2022 PY - 2022 VL - 5 IS - 2 SP - 1 EP - 8 KW - Social Networks KW - Complex networks KW - community detection KW - Community Sensing KW - Graph Clustering DO - 10.22133/ijwr.2022.347854.1124 N2 - 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. UR - https://ijwr.usc.ac.ir/article_164089.html L1 - https://ijwr.usc.ac.ir/article_164089_d32701b7605a2513601952fcadb8e01f.pdf ER -