动态属性网络的语义社区发现及演化分析具有重要研究价值,其包含动态社区发现、社区语义解释及社区演化分析三个任务,但现有方法均难以同时实现.针对该问题,提出一种基于联合非负矩阵分解的方法DAN-NMF(NMF for Dynamic Attributed Networks).DAN-NMF可以统一集成网络拓扑结构信息、节点属性信息及社区演化平滑约束信息,并利用最大最小化优化框架推导相关因子矩阵的迭代更新规则,从而可以直接获得动态社区发现、社区语义解释及社区演化分析结果.在人工合成和真实的动态属性网络进行大量相关实验,结果表明DAN-NMF比最优的基准方法在准确性指标上至少提高了7.3%.此外,在真实动态属性网络上的相关数据分析结果也表明DAN-NMF能够有效地发现动态社区的演化模式,并提供丰富的社区语义解释.
A Community Discovery and Evolution Analysis Method for Dynamic Attributed Networks
The topic of semantic community discovery and evolution analysis in dynamic attributed networks has im-portant research value.It needs to simultaneously accomplish the tasks of dynamic community discovery,community se-mantic interpretation and community evolution analysis,but existing methods are difficult to achieve this goal.In view of this,this paper proposes a method DAN-NMF(NMF for Dynamic Attributed Networks)based on joint nonnegative matrix factorization.DAN-NMF can uniformly integrate network topology information,attribute information and smooth con-straint information from community evolution,and derive iterative update rules of the related factor matrices using the ma-jorization-minimization optimization framework,which helps it to directly obtain the results of dynamic community discov-ery,community semantic interpretation and community evolution analysis.Extensive experiments are conducted on multi-ple synthetic and real-world dynamic attributed networks.The results show that DAN-NMF has improved by at least 7.3%in term of accuracy metric,compared to the optimal baseline.Moreover,the data analysis results on real-world dynamic at-tributed networks also demonstrate that DAN-NMF can effectively discover the evolution patterns of dynamic communities and provide rich community semantic interpretations.
万方数据
维普
