Online Graph Learning in Dynamic Environments

TL;DR

This paper introduces an online graph learning method for dynamic environments that adapts to evolving graph structures in sequential data, with theoretical regret guarantees and superior experimental performance.

Contribution

It develops an online graph learning framework incorporating dynamic priors and pattern prediction, advancing beyond traditional batch methods.

Findings

The method achieves sublinear dynamic regret.

Experimental results outperform state-of-the-art techniques.

The approach effectively tracks evolving graph structures.

Abstract

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.