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A Fast Community Detection Algorithm based on Clustering Coefficient

机译:一种基于聚类系数的快速群落检测算法

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Community detecting has always been a hot topic in the complex network research area, the fast and accurate community detection can provide a good foundation for the research of complex network nature. With the number of network nodes increasing, the structure of network becomes complicated, the traditional community detection becomes more unpractical for they are all based on global information of network. We offered a local community detection algorithm which didn't need to know the whole complex network information, it just beginning with a node as initial community and computing the intensity between the initial node and its adjacent nodes. And add its adjacent nodes to community gradually; eventually get this node community structure. Above all, this method can achieve global network community detection. We applied this method to American College football network and dolphin social network, and experiment results show accuracy and feasibility.
机译:社区检测始终是复杂网络研究区的热门话题,快速准确的社区检测可以为复杂网络性质的研究提供良好的基础。随着网络节点的数量增加,网络结构变得复杂,传统的社区检测对于它们全部基于网络的全球信息而变得更加不实用。我们提供了一个本地社区检测算法,不需要知道整个复杂的网络信息,它刚刚以节点作为初始社区开头,并计算初始节点和其相邻节点之间的强度。并逐步将其相邻节点添加到社区;最终得到这个节点社区结构。最重要的是,这种方法可以实现全球网络社区检测。我们将这种方法应用于美国大学足球网络和海豚社交网络,实验结果表明了准确性和可行性。

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