mlsanityData to 7 Oct 2026

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

arXiv 2107.10234 ↗First posted 21 Jul 2021cs.LG cs.AIcited by 18

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Abstract (arXiv)

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.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 151 of its 287 references that are in the map. First posted on arXiv 21 Jul 2021.

Builds on

151 of 287 references.

References · 151

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50 most cited of 151 All 151 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 6; 2025: 7; 2026: 3.

18 papers.

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  1. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  2. 2024Learn from Global Correlations: Enhancing Evolutionary Algorithm via Spectral GNNcited by 15
  3. 2024A Survey on Graph Condensationcited by 9
  4. 2024A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectivenesscited by 6
  5. 2024Demystifying Higher-Order Graph Neural Networkscited by 4
  6. 2023Tackling Size Generalization of Graph Neural Networks on Biological Data from a Spectral Perspectivecited by 3
  7. 2024Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filteringcited by 3
  8. 2024Transfer Entropy in Graph Convolutional Neural Networkscited by 1
  9. 2025Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localizationcited by 1
  10. 2025Spatio-Temporal Graph Structure Learning for Earthquake Detectioncited by 0
  11. 2025Convexified Message-Passing Graph Neural Networkscited by 0
  12. 2025Learning from Heterophilic Graphs: A Spectral Theory Perspective on the Impact of Self-Loops and Parallel Edgescited by 0
6 more
  1. 2025Rethinking Graph Domain Adaptation: A Spectral Contrastive Perspectivecited by 0
  2. 2025How Do Graph Signals Affect Recommendation: Unveiling the Mystery of Low and High-Frequency Graph Signalscited by 0
  3. 2025Multi-Head Spectral-Adaptive Graph Anomaly Detectioncited by 0
  4. 2026Graph Learning Should Move Beyond Restrictive Views of Spectral and Message-Passing GNNscited by 0
  5. 2026AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagationcited by 0
  6. 2026Delving into Non-Exchangeability for Conformal Prediction in Graph-Structured Multivariate Time Seriescited by 0