mlsanityData to 7 Oct 2026

Clenshaw Graph Neural Networks

arXiv 2210.16508 ↗First posted 29 Oct 2022cs.LG cs.AIcited by 5

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Abstract (arXiv)

Graph Convolutional Networks (GCNs), which use a message-passing paradigm with stacked convolution layers, are foundational methods for learning graph representations. Recent GCN models use various residual connection techniques to alleviate the model degradation problem such as over-smoothing and gradient vanishing. Existing residual connection techniques, however, fail to make extensive use of underlying graph structure as in the graph spectral domain, which is critical for obtaining satisfactory results on heterophilic graphs. In this paper, we introduce ClenshawGCN, a GNN model that employs the Clenshaw Summation Algorithm to enhance the expressiveness of the GCN model. ClenshawGCN equips the standard GCN model with two straightforward residual modules: the adaptive initial residual connection and the negative second-order residual connection. We show that by adding these two residual modules, ClenshawGCN implicitly simulates a polynomial filter under the Chebyshev basis, giving it at least as much expressive power as polynomial spectral GNNs. In addition, we conduct comprehensive experiments to demonstrate the superiority of our model over spatial and spectral GNN models.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 39 of its 64 references that are in the map. First posted on arXiv 29 Oct 2022.

Builds on

39 of 64 references.

References · 39

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27 more
  1. 2020Simple and Deep Graph Convolutional Networkscited by 857
  2. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 821
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  18. 2022How Powerful are Spectral Graph Neural Networkscited by 169
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 0; 2026: 1.

5 papers.

  1. 2024A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directionscited by 13
  2. 2024A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectivenesscited by 6
  3. 2024Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filteringcited by 3
  4. 2024Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approachcited by 1
  5. 2026Spectral Graph Neural Networks with Hermite Polynomials: A Comprehensive Studycited by 0