  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.
