%0 Journal Article
%T A Random Network Ensemble Model Based Generalized Network Community Mining Algorithm<br>基于随机网络集成模型的广义网络社区挖掘算法
%A YANG Bo
%A LIU Jie
%A LIU Da-You
%A <br>杨博
%A 刘杰
%A 刘大有
%J 自动化学报
%D 2012
%I 
%X According to the attributes of nodes and the linkages between them, most real-world complex networks could be assortative and disassortative. Community structures are ubiquitous in both types of networks. The ability to discovery meaningful community structures from both types of networks is fundamental for theoretical research and practical applications. Since the types of exploratory networks to be processed are usually unknown beforehand, it is difficult to determine what specific algorithms should be applied to them to obtain meaningful community structures. To address this issue, a novel concept of generalized network community is proposed in order to unify two concepts of assortative and disassortative communities. Based on a random network ensemble model, a generalized community mining algorithm, called G-NCMA, is proposed. Experimental results demonstrate that the G-NCMA algorithm is able to properly mine potential communities from explorative networks, as well as to determine their respective types.
%K Complex network
%K community mining
%K random network
%K maximum likelihood estimation<br>复杂网络
%K 社区挖掘
%K 随机网络
%K 极大似然估计
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=1393146D229514ED2186C069112EEEFC&yid=99E9153A83D4CB11&vid=16D8618C6164A3ED&iid=94C357A881DFC066&sid=525CF7714FCB18E2&eid=F7C11D7E3E8C5D3F&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=12