Inferring the underlying graph topology that characterizes structured data is pivotal to many graph-based models when pre-defined graphs are not available. This paper focuses on learning graphs in the case of sequential data in dynamic environments. For sequential data, we develop an online version of classic batch graph learning method. To better track graphs in dynamic environments, we assume graphs evolve in certain patterns such that dynamic priors might be embedded in the online graph learning framework. When the information of these hidden patterns is not available, we use history data to predict the evolution of graphs. Furthermore, dynamic regret analysis of the proposed method is performed and illustrates that our online graph learning algorithms can reach sublinear dynamic regret. Experimental results support the fact that our method is superior to the state-of-art methods.

翻译：在没有预定义的图形时,推断结构化数据的基本图形表层对于许多基于图形的模型至关重要。本文件侧重于动态环境中序列数据情况下的学习图表。对于序列数据,我们开发了经典批量图学习方法的在线版本。为了更好地跟踪动态环境中的图表,我们假设图表在某些模式中演进,以便动态前程可能嵌入在线图形学习框架。当这些隐藏模式的信息无法提供时,我们使用历史数据来预测图表的演变。此外,还进行了对拟议方法的动态遗憾分析,并展示了我们的在线图表学习算法能够达到亚线性动态遗憾。实验结果支持了这样一个事实,即我们的方法优于最先进的方法。