Online Graph Learning in Dynamic Environments
This work addresses the challenge of learning graphs for structured data in dynamic settings, which is incremental as it extends existing batch methods to online scenarios.
The paper tackles the problem of inferring graph topologies from sequential data in dynamic environments by developing an online version of a classic batch graph learning method, with results showing superior performance to state-of-the-art methods and achieving sublinear dynamic regret.
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.