A general method of community detection by identifying community centers with affinity propagation
A general method of community detection by identifying community centers with affinity propagation
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一种通过亲和力传播识别社区中心的社区检测通用方法
DOI：
10.1016/j.physa.2015.12.037
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发表时间：
2016-04-01
影响因子：
3.3
通讯作者：
Zhang, Shao-Wu
中科院分区：
文献类型：
--
作者：
Guo, Wei-Feng;Zhang, Shao-Wu
社团结构的检测有助于分析网络的结构和性质。它在现代科学中具有重要的理论意义和实践意义。迄今为止，人们已经提出了大量的算法来检测复杂网络中的社团结构，但大多数算法都适用于特定的网络结构。本文提出了一种新的方法（CDMIC），通过构造网络的相异度距离矩阵，以最大化模块度为准则识别社区中心，来检测未加权、加权、无向、有向和符号网络中的社区。对于一个给定的网络，我们首先估计所有节点对之间的距离，以构建网络的相异性距离矩阵。然后，将该距离矩阵输入到亲和传播（AP）算法中，以提取社区的候选中心集。第三，我们根据这些中心的可用性和责任的总和，将它们按降序排列。最后，我们确定社区结构的中心子集从候选中心集以增量的方式，使模块化最大化。在三个真实网络和一些合成网络上的实验结果表明，我们的CDMIC方法在分类精度和归一化互信息（NMI）方面具有更高的性能，并能够容忍分辨率限制。(C)2015 Elsevier B.V.版权所有。
Detection of community structures is beneficial to analyzing the structures and properties of networks. It is of theoretical interest and practical significance in modern science. So far, a large number of algorithms have been proposed to detect community structures in complex networks, but most of them are suitable for a specific network structure. In this paper, a novel method (called CDMIC) is proposed to detect the communities in un-weighted, weighted, un-directed, directed and signed networks by constructing a dissimilarity distance matrix of network and identifying community centers with maximizing modularity. For a given network, we first estimate the distance between all pairs of nodes for constructing the dissimilarity distance matrix of the network. Then, this distance matrix is input to the affinity propagation (AP) algorithm to extract a candidate center set of community. Thirdly, we rank these centers in descending order according to the sum of their availability and responsibility. Finally, we determine the community structure by selecting the center subset from the candidate center set in an incremental manner to make the modularity maximization. On three real-world networks and some synthetic networks, experimental results show that our CDMIC method has higher performance in terms of classification accuracy and normalized mutual information (NMI), and ability to tolerate the resolution limitation. (C) 2015 Elsevier B.V. All rights reserved.