一种融合节点变化信息的动态社区发现方法

A Dynamic Community Discovery Method via Fusing Node Change Information

动态社区发现旨在检测动态复杂网络中蕴含的社区结构,对于揭示网络的功能及演化模式具有重要研究价值.由于相邻时刻网络的社区结构具有平滑性,前一时刻网络的社区划分信息可以用于监督当前时刻网络的社区划分过程,但已有方法均难以有效提取这些信息来提高动态社区发现性能.针对该问题,提出一种融合节点变化信息的动态社区发现方法(Semi-supervised Nonnegative Matrix Factorization combining Node Change Information,NCI-SeNMF).NCI-SeNMF首先采用k-core分析方法提取前一时刻社区网络的degeneracy-core,并选取degeneracy-core中的节点构造社区隶属先验信息,然后对相邻时刻网络的节点局部拓扑结构变化程度进行量化,并将其用于进一步修正社区隶属先验信息,最后通过半监督非负矩阵分解模型集成社区隶属先验信息进行动态社区发现.在多个人工合成动态网络和真实世界动态网络上进行大量对比实验,结果表明,NCI-SeNMF比现有动态社区发现方法在主要评价指标上至少提升了4.8%.

Dynamic community discovery aims to detect community structure in dynamic complex networks,and has important research value for revealing the functions and evolution patterns of networks.Because the community structure of the adjacent snapshot networks is smooth,the community discovery result of the previous snapshot network can be used to supervise the community discovery process of the current snapshot network.However,existing methods are difficult to ef⁃fectively extract these information to improve the performance of dynamic community discovery.In view of this,a dynamic community discovery method named NCI-SeNMF(Semi-supervised Nonnegative Matrix Factorization combining Node Change Information)is proposed,which can fuse node change information.NCI-SeNMF firstly uses k-core analysis method to extract the degeneracy core of every community network at the previous snapshot,and selects the nodes in the degenera⁃cy core to construct the prior community membership information.Then,it quantifies the change degree of the local topolo⁃gy structure of the nodes in the adjacent snapshot networks,and applies it to further improve the prior community member⁃ship information.Finally,it integrates the prior community membership information through semi-supervised nonnegative matrix factorization model to discover dynamic communities.Extensive comparative experiments have been conducted on several synthetic and real-world dynamic networks,and the results show that NCI-SeNMF improves at least 4.8%in term of core evaluation metrics comparing with the existing dynamic community discovery methods.