A Relationships-based Algorithm for Detecting the Communities in Social Networks
محل انتشار: فصلنامه بین المللی وب پژوهی، دوره: 5، شماره: 2
سال انتشار: 1401
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 174
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شناسه ملی سند علمی:
JR_IJWR-5-2_001
تاریخ نمایه سازی: 20 دی 1401
چکیده مقاله:
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.
کلیدواژه ها:
نویسندگان
Sevda Fotovvat
Department of Computer Science, University of Tabriz, Tabriz, Iran
Habib Izadkhah
Department of Computer Science, University of Tabriz, Tabriz, Iran
Javad Hajipour
Department of Computer Science, University of Tabriz, Tabriz, Iran