基于连边距离矩阵的重叠社区发现
发布时间：2019-07-16 09:51
【摘要】：现有重叠社团发现算法大多直接从相邻连边的相似性出发,不能有效利用网络的多层连边信息,基于此提出了一种基于连边距离矩阵的重叠社区发现算法LDM。首先结合连边—节点—连边随机游走模型,以实现多级连边信息的有效利用;借助模糊聚类方法,处理连边距离矩阵以获取连边社区;最后根据扩展模块度调整和优化重叠社区结构。在人工网络和真实网络上的实验结果表明,所提算法能够有效提高重叠社区发现算法的准确度。
[Abstract]:Most of the existing overlapping community discovery algorithms start directly from the similarity of adjacent connected edges, and can not effectively make use of the multi-layer connected edge information of the network. Based on this, an overlapping community discovery algorithm LDM. based on connected edge distance matrix is proposed. Firstly, the connected edge-node-edge random walk model is combined to realize the effective utilization of multi-level connected edge information; with the help of fuzzy clustering method, the connected edge distance matrix is processed to obtain the connected edge community; finally, the overlapping community structure is adjusted and optimized according to the extended modularity. The experimental results on artificial network and real network show that the proposed algorithm can effectively improve the accuracy of overlapping community discovery algorithm.
【作者单位】： 国家数字交换系统工程技术研究中心;
【基金】：国家“973”计划资助项目(2012CB315901,2012CB315905) 国家自然科学基金创新群体项目(61521003)
【分类号】：O157.5
本文编号：2515003
[Abstract]:Most of the existing overlapping community discovery algorithms start directly from the similarity of adjacent connected edges, and can not effectively make use of the multi-layer connected edge information of the network. Based on this, an overlapping community discovery algorithm LDM. based on connected edge distance matrix is proposed. Firstly, the connected edge-node-edge random walk model is combined to realize the effective utilization of multi-level connected edge information; with the help of fuzzy clustering method, the connected edge distance matrix is processed to obtain the connected edge community; finally, the overlapping community structure is adjusted and optimized according to the extended modularity. The experimental results on artificial network and real network show that the proposed algorithm can effectively improve the accuracy of overlapping community discovery algorithm.
【作者单位】： 国家数字交换系统工程技术研究中心;
【基金】：国家“973”计划资助项目(2012CB315901,2012CB315905) 国家自然科学基金创新群体项目(61521003)
【分类号】：O157.5
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