基于连边距离矩阵的重叠社区发现

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，本文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，其次借助模糊聚类方法，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。

Overlapping community detecting based on link distance matrix

Most overlapping community detecting algorithms ignore the similarity of non-adjacent edges, which reduces the accuracy of overlapping community detection. It is proposed an overlapping community detecting algorithm based on link distance matrix. Firstly, based on the link-node-link random walk model and superposed random walk index, LDM proposed a method to calculate the similarity of the non-adjacent edges in the network, which improves the utility of edge information. Then it uses the fuzzy clustering methods to deal with link distance matrix to obtain the edge community structure. Finally it adjusts and optimizes the overlapping community structure of the network, according to the extension module degree function. Compared with existing algorithms in LFR artificial networks and real networks, the experiment results show that the LDM algorithm can effectively improve the accuracy of overlapping community detection algorithm.