From “Dynamics on Graphs” to “Dynamics of Graphs”: An Adaptive Echo-State Network Solution (Student 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 learning 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 network 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 mapping function and unknown nonlinear map-ping between time series and graphs.

Authors

• Lei Zhang
• Zhiqian Chen Mississippi State University
• Chang-Tien Lu
• Liang Zhao Dalian University of Technology

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