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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

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

February 1, 2023

Authors

Lei Zhang

Virginia Tech


Zhiqian Chen

Mississippi State University


Chang-Tien Lu

Virginia Tech


Liang Zhao

Emory University


Proceedings:

No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Student Abstract and Poster Program

Downloads:

Download PDF

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.

DOI:

10.1609/aaai.v36i11.21692


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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