Advanced Community Identification Model for Social Networks

作     者：Farhan Amin Jin-Ghoo Choi Gyu Sang Choi 

作者机构：Department of Computer EngineeringGachon UniversityGyeonggi-do13120Korea Department of Information and Communication EngineeringYeungnam UniversityGyeongsan38541Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第69卷第11期

页      面：1687-1707页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：This research was supported by the Ministry of Trade,Industry&Energy(MOTIE,Korea)under the Industrial Technology Innovation Program,No.10063130 by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2019R1A2C1006159) by the Ministry of Science and ICT(MSIT),Korea,under the Information Technology Research Center(ITRC)support program(IITP-2019-2016-0-00313)supervised by the Institute for Information&communications Technology Promotion(IITP) 

主　　题：Community detection social network analysis complex networks 

摘      要：Community detection in social networks is a hard problem because of the size,and the need of a deep understanding of network structure and *** several methods with significant effort in this direction have been devised,an outstanding open problem is the unknown number of communities,it is generally believed that the role of influential nodes that are surrounded by neighbors is very *** addition,the similarity among nodes inside the same cluster is greater than among nodes from other ***,the global and local methods of community detection have been getting more ***,in this study,we propose an advanced communitydetection model for social networks in order to identify network communities based on global and local *** proposed model initially detects the most influential nodes by using an Eigen score then performs local expansion powered by label *** process is conducted with the same color till nodes reach maximum ***,the communities are formed,and a clear community graph is displayed to the *** proposed model is completely parameter-free,and therefore,no prior information is required,such as the number of communities,*** perform simulations and experiments using well-known synthetic and real network benchmarks,and compare them with well-known state-of-the-art *** results prove that our model is efficient in all aspects,because it quickly identifies communities in the ***,it can easily be used for friendship recommendations or in business recommendation systems.