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A algorithm based on the local module degree for community detection in complex networks

机译:基于局部模块度的复杂网络社区检测算法

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Community structure is a common property that exists in complex networks. This paper presents a new method which can detect community structure based on the idea of local modularity measure. The algorithm firstly starts from the node which has the max Multifesture of nodes, and finds the candidate node from the candidate set which can reach the maximum of the local modularity measure Q. Secondly, the algorithm merge the node into the community and update the candidate set. At last, clustering results can be received. Since this algorithm only requires local information of the complex network, its time complexity is very low. It can find clustering centers better based on the multifesture value of nodes. Finally, this algorithm is applied to a classical social network, the Zachary network, with satisfactory result, the experiment shows the validity of this method.
机译:社区结构是复杂网络中存在的常见属性。本文提出了一种基于局部模块化测度思想的可检测社区结构的新方法。该算法首先从具有最大Multifesture节点的节点开始,然后从候选集中找到可以达到局部模块化度量Q最大值的候选节点。其次,该算法将该节点合并到社区中并更新候选者放。最后,可以接收聚类结果。由于该算法仅需要复杂网络的本地信息,因此其时间复杂度非常低。它可以基于节点的多特征值更好地找到聚类中心。最后将该算法应用于经典的社交网络Zachary网络,取得了令人满意的结果,实验表明了该方法的有效性。

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