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

机译:一种基于复杂网络社区检测本地模块度的算法

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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共同属性。 本文介绍了一种新方法,可以根据局部模块化测量的思想检测群落结构。 该算法首先从具有节点的最大多重度的节点开始,并从候选集中找到可以达到本地模块化测量Q的最大值的候选节点。其次,该算法将节点合并到社区中并更新候选者 放。 最后,可以收到聚类结果。 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。 它可以根据节点的多重值值找到聚类中心。 最后,该算法应用于经典的社交网络,Zachary网络,结果令人满意,实验表明了这种方法的有效性。

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