基于中心节点的动态扩散社团划分算法

Dynamic Diffusion Community Detection Algorithm Based on Central Node

社团划分是复杂网络研究中的关键研究方向之一。现有的绝大多数工作都聚焦于网络拓扑而忽略网络上的动态过程,针对此问题提出一种基于中心节点的动态扩散社团划分算法。首先,提出基于非回溯游走路径数的节点中心性评价指标;其次,为了对网络上发生的多尺度社交互动模式进行建模,找到一种新的边隶属度向量表示节点的社团归属情况,将中心节点与社团划分联系在一起,用动态系统表示社团成员的动态分配过程进而完成重叠社团划分;最后,为验证所提算法的有效性,将其应用于真实网络和人工网络,实验结果表明,所提算法在划分精度上有很大的优势。

Community detection is one of the key research directions in the study of complex networks.Most of the existing work focuses on network topology but ignores the dynamic process on the network.Thus,a dynamic diffusion community detection algorithm based on central node is proposed.First,a node centrality measure metric is proposed based on the number of non-backtracking path.Then,in order to model the multi-scale social interaction mode occurring on the network,a new edge membership vector is designed to represent the community belonging of nodes which links the central node with community detection.Besides,a dynamic system is designed to represent the dynamic distribution process of community members to complete overlapping community detection.Finally,the proposed algorithm is applied to real networks and artificial networks to verify its effectiveness.The experimental results show the proposed algorithm has great advantages in detection accuracy.