Detecting and refining overlapping regions in complex networks with three-way decisions
Detecting and refining overlapping regions in complex networks with three-way decisions
复制标题
通过三向决策检测和细化复杂网络中的重叠区域
DOI：
10.1016/j.ins.2016.08.087
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发表时间：
2016-12
影响因子：
8.1
通讯作者：
Guoyin Wang
中科院分区：
文献类型：
--
作者：
Hong Yu;Peng Jiao;Yiyu Yao;Guoyin Wang
群落的识别对于理解复杂网络的结构和功能特性至关重要。已经开发了许多方法和算法来检测重叠社区。一个尚未得到很好解决的问题是，在群落的形成和发展过程中，重叠区域中的顶点之间的关系或差异。本文研究了一种既能检测重叠社区又能细化重叠区域的方法。利用区间集给出了一个社区的三向表示，并将社区检测问题形式化为三向聚类。我们提出了四种宏观类型和八种微观类型的顶点来刻画重叠区域中的成员，以求精。通过将节点划分为核心节点、骨骼节点和琐碎节点，提出了一种重叠社区检测算法。该算法的主要策略是寻找初始聚类核心，根据新的适应度函数将核心扩展到一个初步社区，并基于三向决策策略合并平凡的顶点。在真实社会网络和计算机生成的人工网络上的实验结果表明了该方法的有效性和高效性。
The identification of communities is crucial to an understanding of the structural and functional properties of a complex network. Many methods and algorithms have been developed to detect overlapping communities. A problem that has not been addressed satisfactorily is the relationship or difference between vertices in overlapping regions during the formation and growth of communities. This paper investigates methods that not only detect the overlapping communities but also refine the overlapping regions. We give a three-way representation of a community by using interval sets and re-formalize the problem of community detection as three-way clustering. We suggest four macro types and eight micro types of vertices to characterize members in overlapping regions for their refinement. We propose an overlapping community detection algorithm by classifying the vertices into core vertices, bone vertices, and trivial vertices. The main strategy of this algorithm is to find an initial cluster core, to expand the core to a preliminary community according to a new fitness function, and to merge trivial vertices based on three-way decision strategies. The experimental results on both real-world social networks and computer-generated artificial networks show the effectiveness and efficiency of the proposed methods.
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DOI：
10.1007/978-1-4471-4555-4
发表时间：
2012-12
期刊：
--
影响因子：
--
作者：
N. Ramzan;R. V. Zwol;Jong-Seok Lee;Kai Clver;Xiansheng Hua
通讯作者：
N. Ramzan;R. V. Zwol;Jong-Seok Lee;Kai Clver;Xiansheng Hua
一种基于图中社区检测的快速弱主题发现算法
影响因子：
3
作者：
Jia C;Carson MB;Yu J
通讯作者：
Yu J
影响因子：
3.6
作者：
C. Schechter
通讯作者：
C. Schechter
DOI：
10.1073/pnas.0601602103
发表时间：
2006-06-06
影响因子：
11.1
作者：
Newman, M. E. J.
通讯作者：
Newman, M. E. J.
影响因子：
2.4
作者：
Psorakis, Ioannis;Roberts, Stephen;Sheldon, Ben
通讯作者：
Sheldon, Ben