时序网络上异常演化模式研究

Study on Anomalous Evolution Pattern on Temporal Networks

许多异常子图检测方法已经被成功应用于社交网络中的事件检测、道路网络中的交通拥堵检测等任务中。然而,在属性图中异常子图的动态演化方面,鲜有研究开展。文中提出了一种名为动态演化多异常子图扫描(DE-MASS)的方法,用于检测属性图上多个异常子图的演化模式,这是第一个捕捉相邻时间片上多个相连异常子图的动态图研究。DE-MASS在微博数据集、计算机流量数据集上的表现优于其他基准方法,并检测到3个实际应用中异常子图的演化模式:城市道路网络中的交通拥堵检测(北京、天津和南京)、社交网络(微博)中的事件检测和计算机流量网络中的网络攻击检测。

The competitive methods for anomalous subgraphs detection have been successfully applied to tasks like event detection in social networks,traffic congestion detection in road networks,etc.However,few studies have been initiated in the dynamic evolution of anomalous subgraphs in attributed graphs.For multiple anomalous subgraph evolving pattern,it is the first dynamic graph-based study to capture multi-anomalies connected on time intervals.This study proposes an approach,namely dynamic evolution of multiple anomalous subgraphs scanning(DE-MASS),to detect the most anomalous evolutionary pattern,which consists of multiple anomalous subgraphs on attributed graphs.The DE-MASS outperforms the competitive baselines in the Weibo real dataset,computer traffic real dataset,and captures the evolution patterns of anomalous subgraphs on three real-world applications:traffic congestion detection in urban road networks(Beijing,Tianjin,and Nanjing in China),event detection in the social network(Weibo)and cyber-attack detection in computer traffic network.