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作 者:Cairui YAN Huifang MA Qingqing LI Fanyi YANG Zhixin LI
机构地区:[1]College of Computer Science and Engineering,Northwest Normal University,Lanzhou 730070,China [2]Guangxi Key Lab of Multi-source Information Mining and Security,Guangxi Normal University,Guilin 541004,China
出 处:《Frontiers of Computer Science》2023年第5期89-101,共13页中国计算机科学前沿(英文版)
基 金:supported by the Industrial Support Project of Gansu Colleges(2022CYZC-11);National Natural Science Foundation of China(61363058,61966004);Northwest Normal University Young Teachers Research Capacity Promotion Play(NWNU-LKQN2019-2);Natural Science Foundation of Gansu Province(21JR7RA114).
摘 要:Community search is an important problem in network analysis,which has attracted much attention in recent years.As a query-oriented variant of community detection problem,community search starts with some given nodes,pays more attention to local network structures,and gets personalized resultant communities quickly.The existing community search method typically returns a single target community containing query nodes by default.This is a strict requirement and does not allow much flexibility.In many realworld applications,however,query nodes are expected to be located in multiple communities with different semantics.To address this limitation of existing methods,an efficient spectral-based Multi-Scale Community Search method(MSCS)is proposed,which can simultaneously identify the multi-scale target local communities to which query node belong.In MSCS,each node is equipped with a graph Fourier multiplier operator.The access of the graph Fourier multiplier operator helps nodes to obtain feature representations at various community scales.In addition,an efficient algorithm is proposed for avoiding the large number of matrix operations due to spectral methods.Comprehensive experimental evaluations on a variety of real-world datasets demonstrate the effectiveness and efficiency of the proposed method.
关 键 词:community search MULTI-SCALE spectral wavelets
分 类 号:TP3[自动化与计算机技术—计算机科学与技术]
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