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Community Structure Identification in Networks via Detecting Community Center Method

机译:通过检测社区中心方法识别网络中的社区结构

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Identifying community structure in a simple network G(V, E) is essentially a clustering problem. The most important step to solve this problem is to determine the number of communities and find out community centers. Therefore, each node's local centrality CL and local dissimilarity δ are introduced. Nodes which have both high local centrality and local dissimilarity are chosen as the centers of the communities and the number of centers equals to the number of communities. After that we assign each node to its corresponding community by using Kmeans clustering method or Hierarchical clustering method. In the last part we applied our approach to a real relationship network and made a very satisfactory result.
机译:识别简单网络G(V,E)中的社区结构本质上是一个聚类问题。解决此问题的最重要步骤是确定社区数量并找出社区中心。因此,引入了每个节点的局部中心度CL和局部不相似度δ。选择具有较高局部中心性和局部不相似性的节点作为社区的中心,中心的数量等于社区的数量。之后,我们使用Kmeans聚类方法或分层聚类方法将每个节点分配给其相应的社区。在最后一部分中,我们将我们的方法应用于一个真实的关系网络,并取得了非常令人满意的结果。

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