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Methods to find community based on edge centrality

机译：基于边缘中心性的社区寻找方法

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Divisive algorithms are of great importance for community detection in complex networks. One algorithm proposed by Girvan and Newman (GN) based on an edge centrality named betweenness, is a typical representative of this field. Here we studied three edge centralities based on network topology, walks and paths respectively to quantify the relevance of each edge in a network, and proposed a divisive algorithm based on the rationale of GN algorithm for finding communities that removes edges iteratively according to the edge centrality values in a certain order. In addition, we gave a comparison analysis of these measures with the edge betweenness and information centrality. We found the principal difference among these measures in the partition procedure is that the edge centrality based on walks first removes the edge connected with a leaf vertex, but the others first delete the edge as a bridge between communities. It indicates that the edge centrality based on walks is harder to uncover communities than other edge centralities. We also tested these measures for community detection. The results showed that the edge information centrality outperforms other measures, the edge centrality based on walks obtains the worst results, and the edge betweenness gains better performance than the edge centrality based on network topology. We also discussed our method's efficiency and found that the edge centrality based on walks has a high time complexity and is not suitable for large networks.
机译：区分算法对于复杂网络中的社区检测非常重要。 Girvan和Newman（GN）基于边缘中心性（介于中间性之间）提出的一种算法是该领域的典型代表。在这里，我们基于网络拓扑研究了三个边缘中心点，分别通过步行和路径来量化网络中每个边缘点的相关性，并提出了一种基于GN算法原理的分裂算法，该算法可找到根据边缘中心点迭代地去除边缘点的社区值按一定顺序排列。另外，我们对这些措施与边缘之间的中间性和信息中心性进行了比较分析。我们发现这些方法在分区过程中的主要区别在于，基于游走的边缘中心性首先删除与叶顶点相连的边缘，而其他首先删除边缘作为社区之间的桥梁。这表明基于步行的边缘中心性比其他边缘中心性更难发现社区。我们还测试了这些措施以进行社区发现。结果表明，边缘信息中心性优于其他度量，基于游走的边缘中心性最差，边缘间性比基于网络拓扑的边缘中心性更好。我们还讨论了该方法的效率，发现基于游走的边缘中心性具有较高的时间复杂度，不适用于大型网络。

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