摘要： 目前大多数用于社团发现问题的模型只适用于静态网络而忽视了时序信息,因此,无法较好地建模真实世界数据。针对该问题,提出一种基于度修正随机块模型的演化社团发现模型。根据演化聚类框架的原理,基于社团隶属矩阵将一个正则项引入到度修正随机块模型的目标函数中。利用网络交叉验证方法进行模型选择,处理社团个数随时间变化的演化网络,从而克服由于假定社团个数为常量而导致的与真实世界数据不相符合的问题。实验结果表明,与经典的动态随机块模型和FacetNet相比,该模型具有较高的准确性和较低的误差率。

关键词: 演化网络, 演化分析, 社团发现, 模型选择, 随机块模型, 节点特性

Abstract: Nowadays,many models for community detection are designed only for static networks,which ignore the temporal information and are always not ideal to model the real world data.In order to solve this problem,an evolving community detection model based on the degree-corrected block model is proposed.According to the theory of the framework of evolutionary clustering,the model introduces a regularization term based on the community membership matrix into the objective function of the degree-corrected stochastic block model.The network cross-validation approach is utilized for model selection,so the proposed method is able to deal with evolving networks with variational numbers of communities.In this way,it overcomes the problem of assuming the number of communities as a constant,which is not consistent with real world data.Experimental results show that the model has a better performance with higher accuracy and lower error rate compared with the classical dynamic stochastic block model and the FacetNet.

Key words: evolving network, evolutionary analysis, community detection, model selection, stochastic block model, node peculiarity

中图分类号: 

• TP18