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Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks (2107.10234v5)

Published 21 Jul 2021 in cs.LG and cs.AI

Abstract: Deep learning's performance has been extensively recognized recently. Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does not easily manage. Since most GNNs were created using distinct theories, direct comparisons are impossible. Prior research has primarily concentrated on categorizing existing models, with little attention paid to their intrinsic connections. The purpose of this study is to establish a unified framework that integrates GNNs based on spectral graph and approximation theory. The framework incorporates a strong integration between spatial- and spectral-based GNNs while tightly associating approaches that exist within each respective domain.

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Authors (10)
  1. Zhiqian Chen (21 papers)
  2. Fanglan Chen (11 papers)
  3. Lei Zhang (1689 papers)
  4. Taoran Ji (12 papers)
  5. Kaiqun Fu (11 papers)
  6. Liang Zhao (353 papers)
  7. Feng Chen (261 papers)
  8. Lingfei Wu (135 papers)
  9. Charu Aggarwal (38 papers)
  10. Chang-Tien Lu (54 papers)
Citations (12)
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