Community structure detection based on Potts model and network's spectral characterization
Community structure detection based on Potts model and network's spectral characterization
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基于Potts模型和网络谱表征的群落结构检测
DOI：
10.1209/0295-5075/97/48005
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发表时间：
2012-02-01
期刊：
影响因子：
1.8
通讯作者：
Zhang, Xiang-Sun
中科院分区：
文献类型：
--
作者：
Li, Hui-Jia;Wang, Yong;Zhang, Xiang-Sun
波茨模型被用于揭示复杂网络中的社区结构。然而，它无法有效地揭示诸多重要信息，比如社区的最优数量以及隐藏在网络中的重叠节点。与以往研究不同，我们通过利用马尔可夫过程建立了一个新框架，用于研究波茨模型在社区结构检测方面的动力学，该框架具有清晰的数学解释。基于我们的框架，我们证明了自旋值的局部一致行为能够自然地揭示给定网络的层次化社区结构。关于最优社区结构的关键拓扑信息也可以从马尔可夫过程的谱特征中推断出来。我们开发了一种两阶段算法来检测社区结构。该算法的有效性和效率已从理论上进行了分析，并通过实验进行了验证。版权所有（C）欧洲物理学会（EPLA），2012年
The Potts model was used to uncover community structure in complex networks. However, it could not reveal much important information such as the optimal number of communities and the overlapping nodes hidden in networks effectively. Differently from the previous studies, we established a new framework to study the dynamics of Potts model for community structure detection by using the Markov process, which has a clear mathematic explanation. Based on our framework, we showed that the local uniform behavior of spin values could naturally reveal the hierarchical community structure of a given network. Critical topological information regarding the optimal community structure could also be inferred from spectral signatures of the Markov process. A two-stage algorithm to detect community structure is developed. The effectiveness and efficiency of the algorithm has been theoretically analyzed as well as experimentally validated. Copyright (C) EPLA, 2012