Publication

Fast and adaptive dynamics-on-graphs to dynamics-of-graphs translation

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Last modified
  • 06/25/2025
Type of Material
Authors
    Lei Zhang, Virginia TechZhiqian Cheb, Mississippi State UniversityChang-Tien Lu, Virginia TechLiang Zhao, Emory University
Language
  • English
Date
  • 2023-11-17
Publisher
  • Frontiers
Publication Version
Copyright Statement
  • © 2023 Zhang, Chen, Lu and Zhao.
License
Final Published Version (URL)
Title of Journal or Parent Work
Volume
  • 6
Start Page
  • 1274135
Grant/Funding Information
  • The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Supplemental Material (URL)
Abstract
  • Numerous networks in the real world change with time, producing dynamic graphs such as human mobility networks and brain networks. Typically, the “dynamics on graphs” (e.g., changing node attribute values) are visible, and they may be connected to and suggestive of the “dynamics of graphs” (e.g., evolution of the graph topology). Due to two fundamental obstacles, modeling and mapping between them have not been thoroughly explored: (1) the difficulty of developing a highly adaptable model without solid hypotheses and (2) the ineffectiveness and slowness of processing data with varying granularity. To solve these issues, we offer a novel scalable deep echo-state graph dynamics encoder for networks with significant temporal duration and dimensions. A novel neural architecture search (NAS) technique is then proposed and tailored for the deep echo-state encoder to ensure strong learnability. Extensive experiments on synthetic and actual application data illustrate the proposed method's exceptional effectiveness and efficiency.
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Keywords
Research Categories
  • Biology, Neuroscience
  • Artificial Intelligence

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