mlsanityData to 7 Oct 2026

Improving Your Graph Neural Networks: A High-Frequency Booster

arXiv 2210.08251 ↗First posted 15 Oct 2022cs.LGcited by 4

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Abstract (arXiv)

Graph neural networks (GNNs) hold the promise of learning efficient representations of graph-structured data, and one of its most important applications is semi-supervised node classification. However, in this application, GNN frameworks tend to fail due to the following issues: over-smoothing and heterophily. The most popular GNNs are known to be focused on the message-passing framework, and recent research shows that these GNNs are often bounded by low-pass filters from a signal processing perspective. We thus incorporate high-frequency information into GNNs to alleviate this genetic problem. In this paper, we argue that the complement of the original graph incorporates a high-pass filter and propose Complement Laplacian Regularization (CLAR) for an efficient enhancement of high-frequency components. The experimental results demonstrate that CLAR helps GNNs tackle over-smoothing, improving the expressiveness of heterophilic graphs, which adds up to 3.6% improvement over popular baselines and ensures topological robustness.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 28 of its 33 references that are in the map. First posted on arXiv 15 Oct 2022.

Builds on

28 of 33 references.

References · 28

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  6. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  7. 2019Simplifying Graph Convolutional Networkscited by 1,470
  8. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  9. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  10. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  11. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  12. 2020Simple and Deep Graph Convolutional Networkscited by 857
16 more
  1. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 821
  2. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 679
  3. 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 592
  4. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 544
  5. 2019Multi-scale Attributed Node Embeddingcited by 543
  6. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  7. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 409
  8. 2021Beyond Low-frequency Information in Graph Convolutional Networkscited by 361
  9. 2020Graph Neural Networks with Heterophilycited by 202
  10. 2021BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximationcited by 195
  11. 2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
  12. 2021AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filtercited by 43
  13. 2015Network Lasso: Clustering and Optimization in Large Graphscited by 42
  14. 2020Rethinking Graph Regularization for Graph Neural Networkscited by 36
  15. 2020Spectral Graph Attention Network with Fast Eigen-approximationcited by 18
  16. 2021Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphscited by 18

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Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2024Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approachcited by 17
  2. 2023Feature Expansion for Graph Neural Networkscited by 2
  3. 2025Topology-aware Neural Flux Prediction Guided by Physicscited by 2
  4. 2024Graph Contrastive Learning with Low-Rank Regularization and Low-Rank Attention for Noisy Node Classificationcited by 1