mlsanityData to 14 Oct 2026

Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials

arXiv 2305.19872 ↗First posted 31 May 2023cs.LGcited by 8

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Abstract (arXiv)

Heterogeneous Graph Neural Networks (HGNNs) have gained significant popularity in various heterogeneous graph learning tasks. However, most existing HGNNs rely on spatial domain-based methods to aggregate information, i.e., manually selected meta-paths or some heuristic modules, lacking theoretical guarantees. Furthermore, these methods cannot learn arbitrary valid heterogeneous graph filters within the spectral domain, which have limited expressiveness. To tackle these issues, we present a positive spectral heterogeneous graph convolution via positive noncommutative polynomials. Then, using this convolution, we propose PSHGCN, a novel Positive Spectral Heterogeneous Graph Convolutional Network. PSHGCN offers a simple yet effective method for learning valid heterogeneous graph filters. Moreover, we demonstrate the rationale of PSHGCN in the graph optimization framework. We conducted an extensive experimental study to show that PSHGCN can learn diverse heterogeneous graph filters and outperform all baselines on open benchmarks. Notably, PSHGCN exhibits remarkable scalability, efficiently handling large real-world graphs comprising millions of nodes and edges. Our codes are available at https://github.com/ivam-he/PSHGCN.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 39 of its 53 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

39 of 53 references.

References · 39

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27 more
  1. 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 595
  2. 2020Heterogeneous Graph Transformercited by 582
  3. 2019Graph Transformer Networkscited by 523
  4. 2020MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embeddingcited by 304
  5. 2021Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Predictioncited by 275
  6. 2019Hyperbolic Graph Neural Networkscited by 244
  7. 2021BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximationcited by 195
  8. 2021Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networkscited by 176
  9. 2022How Powerful are Spectral Graph Neural Networkscited by 169
  10. 2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
  11. 2021Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learningcited by 130
  12. 2019Representation Learning for Attributed Multiplex Heterogeneous Networkcited by 91
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  14. 2022Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisitedcited by 87
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  22. 2020Scalable Graph Neural Networks for Heterogeneous Graphscited by 31
  23. 2022Multiplex Heterogeneous Graph Convolutional Networkcited by 27
  24. 2023How Graph Convolutions Amplify Popularity Bias for Recommendation?cited by 10
  25. 2022Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networkscited by 9
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 2.

8 papers.

  1. 2024Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domainscited by 5
  2. 2024Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approachcited by 2
  3. 2024Loss-aware Curriculum Learning for Heterogeneous Graph Neural Networkscited by 1
  4. 2025Representation Learning with Mutual Influence of Modalities for Node Classification in Multi-Modal Heterogeneous Networkscited by 1
  5. 2025Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detectioncited by 1
  6. 2025Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systemscited by 1
  7. 2026Modality as Heterogeneity: Node Splitting and Graph Rewiring for Multimodal Graph Learningcited by 1
  8. 2026GCN-MPPR: Enhancing the Propagation of Message Passing Neural Networks via Motif-Based Personalized PageRankcited by 0