Local Detection of Communities by Neural-Network Dynamics
Local Detection of Communities by Neural-Network Dynamics
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DOI：
10.1007/978-3-642-40728-4_7
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发表时间：
2013-09
期刊：
影响因子：
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通讯作者：
H. Okamoto
中科院分区：
文献类型：
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作者：
H. Okamoto
社区结构是各种现实世界网络的标志。本文提出了一种局部检测网络社区的方法。该方法被描述为“本地”，因为它的目的是在不知道网络中所有社区的情况下找到给定源节点所属的社区。受神经网络中神经元活动稳定传播的可能机制的启发，我们设计了这种方法。为了证明我们的方法的有效性，研究了合成基准网络和真实社会网络中社区的局部检测。该方法检测到的群落结构与这些网络的正确群落结构完全一致。
Community structure is a hallmark of a variety of real-world networks. Here we propose a local method for detecting communities in networks. The method is described as ‘local’ because it is intended to find the community to which a given source node belongs without knowing all the communities in the network. We have devised this method inspired by possible mechanisms for stable propagation of neuronal activities in neural networks. To demonstrate the effectiveness of our method, local detection of communities in synthetic benchmark networks and real social networks is examined. The community structure detected by our method is perfectly consistent with the correct community structure of these networks.