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- research-articleAugust 2024
PolyFormer: Scalable Node-wise Filters via Polynomial Graph Transformer
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 2118–2129https://doi.org/10.1145/3637528.3671849Spectral Graph Neural Networks have demonstrated superior performance in graph representation learning. However, many current methods focus on employing shared polynomial coefficients for all nodes, i.e., learning node-unified filters, which limits the ...
- research-articleMay 2024
Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials
WWW '24: Proceedings of the ACM Web Conference 2024Pages 685–696https://doi.org/10.1145/3589334.3645515Heterogeneous 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 ...
- research-articleApril 2024
Convolutional neural networks on graphs with chebyshev approximation, revisited
NIPS '22: Proceedings of the 36th International Conference on Neural Information Processing SystemsArticle No.: 527, Pages 7264–7276Designing spectral convolutional networks is a challenging problem in graph learning. ChebNet, one of the early attempts, approximates the spectral graph convolutions using Chebyshev polynomials. GCN simplifies ChebNet by utilizing only the first two ...
- research-articleJune 2024
BernNet: learning arbitrary graph spectral filters via bernstein approximation
NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing SystemsArticle No.: 1091, Pages 14239–14251Many representative graph neural networks, e.g., GPR-GNN and ChebNet, approximate graph convolutions with graph spectral filters. However, existing work either applies predefined filter weights or learns them without necessary constraints, which may lead ...
- research-articleAugust 2021
Approximate Graph Propagation
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningPages 1686–1696https://doi.org/10.1145/3447548.3467243Efficient computation of node proximity queries such as transition probabilities, Personalized PageRank, and Katz are of fundamental importance in various graph mining and learning tasks. In particular, several recent works leverage fast node proximity ...