From “Dynamics on Graphs” to “Dynamics of Graphs”: An Adaptive Echo-State Network Solution

Lei Zhang, Zhiqian Chen, Liang Zhao

Abstract

Many real-world networks evolve over time, which results in dynamic graphs such as human mobility networks and brain networks. Usually, the “dynamics on graphs” (e.g., node attribute values evolving) are observable, and may be related to and indicative of the underlying “dynamics of graphs” (e.g., evolving of the graph topology). Traditional RNN-based methods are not adaptive or scalable for learn- ing the unknown mappings between two types of dynamic graph data. This study presents a AD-ESN, and adaptive echo state network that can automatically learn the best neural net- work architecture for certain data while keeping the efficiency advantage of echo state networks. We show that AD-ESN can successfully discover the underlying pre-defined map- ping function and unknown nonlinear map-ping between time series and graphs.

Lei Zhang, Zhiqian Chen, Chang-Tien Lu, Liang Zhao: From “Dynamics on Graphs” to “Dynamics of Graphs”: An Adaptive Echo-State Network Solution, AAAI 2022: 13111-13112