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A General Method for Detecting Community Structures in Complex Networks

机译:一种检测复杂网络中社区结构的一般方法

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We present a general method for detecting communities and their sub-structures in a complex network. The novelty of the method is to separate the network model and the community detection model. Network connectivity and influence spreading models are used as examples for network models. Depending on the network model, different communities and sub-structures can be found. We illustrate the results with two empirical network topologies. In these cases the strongest detected communities are very similar for the two network models. We use a community detection method that is based on searching local maxima of an influence measure describing interactions between nodes in a network.
机译:我们介绍了一种用于检测复杂网络中的社区及其子结构的一般方法。该方法的新颖性是分离网络模型和社区检测模型。网络连接和影响扩展模型用作网络模型的示例。根据网络模型,可以找到不同的社区和子结构。我们用两个经验网络拓扑说明了结果。在这些情况下,最强的检测到的社区对于两个网络模型非常相似。我们使用基于搜索局部Maxima的社区检测方法来描述网络中节点之间的交互的影响措施。

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