School of Computer Science and Technology, Xidian University, Xi'an 710071, People's Republic of China.
Department of Computer Science, University of British Columbia Okanagan, Kelowna, British Columbia, Canada V1V 1V7, Canada.
IET Syst Biol. 2014 Jun;8(3):116-25. doi: 10.1049/iet-syb.2013.0039.
Community detection has been extensively studied in the past decades largely because of the fact that community exists in various networks such as technological, social and biological networks. Most of the available algorithms, however, only focus on the properties of the vertices, ignoring the roles of the edges. To explore the roles of the edges in the networks for community discovery, the authors introduce the novel edge centrality based on its antitriangle property. To investigate how the edge centrality characterises the community structure, they develop an approach based on the edge antitriangle centrality with the isolated vertex handling strategy (EACH) for community detection. EACH first calculates the edge antitriangle centrality scores for all the edges of a given network and removes the edge with the highest score per iteration until the scores of the remaining edges are all zero. Furthermore, EACH is characterised by being free of the parameters and independent of any additional measures to determine the community structure. To demonstrate the effectiveness of EACH, they compare it with the state-of-the art algorithms on both the synthetic networks and the real world networks. The experimental results show that EACH is more accurate and has lower complexity in terms of community discovery and especially it can gain quite inherent and consistent communities with a maximal diameter of four jumps.
社区发现在过去几十年中得到了广泛的研究，主要是因为社区存在于各种网络中，如技术、社会和生物网络。然而，大多数现有的算法仅关注顶点的属性，而忽略了边的作用。为了探索网络中边在社区发现中的作用，作者引入了基于其反对三角形性质的新边中心性。为了研究边中心性如何刻画社区结构，他们开发了一种基于边反对三角形中心性和孤立顶点处理策略（EACH）的方法进行社区检测。EACH 首先计算给定网络中所有边的边反对三角形中心性得分，并在每次迭代中删除得分最高的边，直到剩余边的得分都为零。此外，EACH 的特点是没有参数，并且不依赖于任何其他确定社区结构的措施。为了证明 EACH 的有效性，他们在合成网络和真实世界网络上与最先进的算法进行了比较。实验结果表明，EACH 在社区发现方面更加准确，复杂度更低，特别是它可以获得具有最大直径为四个跳跃的相当固有和一致的社区。
