文献详情
Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization
文献类型：期刊
通讯作者：Ying, WQ (reprint author), South China Univ Technol, Sch Software Engn, Guangzhou 510006, Guangdong, Peoples R China.; Wu, Y (reprint author), Guangzhou Univ, Sch Comp Sci & Educ Software, Guangzhou 510006, Guangdong, Peoples R China.
期刊名称：PROCESSES
年：2019
卷：7
期：2
关键词：complex networks; community detection; multi-objective optimization; evolutionary algorithms; parallel island models
所属部门：统计学院
摘要：Detecting community structures helps to reveal the functional units of complex networks. In this paper, the community detection problem is regarded as a modularity-based multi-objective optimization problem (MOP), and a parallel conical area community detection algorithm (PCACD) is designed to solve this MOP effectively and efficiently. In consideration of the global properties of the selection and update mechanisms, PCACD employs a global island model and targeted elitist migration policy in a ...More
Detecting community structures helps to reveal the functional units of complex networks. In this paper, the community detection problem is regarded as a modularity-based multi-objective optimization problem (MOP), and a parallel conical area community detection algorithm (PCACD) is designed to solve this MOP effectively and efficiently. In consideration of the global properties of the selection and update mechanisms, PCACD employs a global island model and targeted elitist migration policy in a conical area evolutionary algorithm (CAEA) to discover community structures at different resolutions in parallel. Although each island is assigned only a portion of all sub-problems in the island model, it preserves a complete population to accomplish the global selection and update. Meanwhile the migration policy directly migrates each elitist individual to an appropriate island in charge of the sub-problem associated with this individual to share essential evolutionary achievements. In addition, a modularity-based greedy local search strategy is also applied to accelerate the convergence rate. Comparative experimental results on six real-world networks reveal that PCACD is capable of discovering potential high-quality community structures at diverse resolutions with satisfactory running efficiencies. ...Hide

DOI：10.3390/pr7020111
百度学术：Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization
语言：外文
人气指数：3
浏览次数：3
基金：Natural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of Guangdong Province [2015A030313204, 2017A030310013, 2018A030313389]; Pearl River S&T Nova Program of Guangzhou [2014J2200052]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [61203310, 61503087]; Fundamental Research Funds for the Central Universities, SCUTFundamental Research Funds for the Central Universities [2017MS043]; Major Research and Development Program for Industrial Technology of Guangzhou City [201802010025]; Platform Development Program for Innovation and Entrepreneurship at Colleges in Guangzhou [2019PT103]
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