Local Detection of Communities by Attractor Neural-Network Dynamics
Local Detection of Communities by Attractor Neural-Network Dynamics
复制标题
通过吸引子神经网络动力学对社区进行本地检测
DOI：
10.1007/978-3-319-09903-3_6
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Hiroshi Okamoto
中科院分区：
文献类型：
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作者：
レラトーレイサ;加藤毅;長野希美;Hiroshi Okamoto
社区结构是各种现实世界网络的标志。发展有效的网络社区发现方法一直是网络科学的核心问题。在这里，我们提出了一种方法来检测网络中的社区。我们设计了这种方法的灵感来自细胞组装假说，这一直是在神经科学的流行假设之一。细胞组装假说认为，编码同一项目的神经元往往相互连接，从而形成一个“细胞组装”;对该项目的记忆回忆与属于细胞组装的神经元的持续激活有关。在这里，我们将社区与细胞集合进行比较，并通过使用描述大脑记忆回忆的神经网络动态来检查社区检测。为了证明所提出的方法的有效性，在合成的基准网络和真实的社交网络的社区的局部检测进行检查。我们的方法检测到的社区结构与这些网络的正确社区结构高度一致。
Community structure is a hallmark of a variety of real-world networks. Development of effective and efficient methods for detecting communities in networks has been a central issue of network science. Here we propose a method for detecting communities in networks. We have devised this method inspired by the cell assembly hypothesis, which has been one of the prevailing hypotheses in neuroscience. The cell assembly hypothesis states that neurons coding the same item tend to be mutually connected, thus forming a ‘cell assembly’; memory recall of this item is associated with sustained activation of neurons belonging to the cell assembly. Here we compare communities to cell assemblies and examine community detection by use of the neural-network dynamics describing memory recall in the brain. To demonstrate the effectiveness of the proposed method, local detection of communities in synthetic benchmark networks and real social networks is examined. The community structure detected by our method is highly consistent with the correct community structure of these networks.