Papers › Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering

Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering

10 May 2023arXiv:2305.06102archive 2025-07-28

Mingqi Yang, Wenjie Feng, Yanming Shen, Bryan Hooi

Proposing an effective and flexible matrix to represent a graph is a fundamental challenge that has been explored from multiple perspectives, e.g., filtering in Graph Fourier Transforms. In this work, we develop a novel and general framework which unifies many existing GNN models from the view of parameterized decomposition and filtering, and show how it helps to enhance the flexibility of GNNs while alleviating the smoothness and amplification issues of existing models. Essentially, we show that the extensively studied spectral graph convolutions with learnable polynomial filters are constrained variants of this formulation, and releasing these constraints enables our model to express the desired decomposition and filtering simultaneously. Based on this generalized framework, we develop models that are simple in implementation but achieve significant improvements and computational efficiency on a variety of graph learning tasks. Code is available at https://github.com/qslim/PDF.

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qslim/pdf officialmentioned in papermentioned on GitHubpytorchMIT report
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filter_train_set qslim/PDF/ogbg/mol/utils/filter.py official repository unverified MIT (permissive) · 95d367ec79503a9a · report
power_computation qslim/PDF/utils/basis_transform.py official repository unverified MIT (permissive) · fa3e7a28a824b389 · report
to_dense qslim/PDF/utils/basis_transform.py official repository unverified MIT (permissive) · 9fe73cedf564403e · report
to_dense_adj qslim/PDF/utils/basis_transform.py official repository unverified MIT (permissive) · 2357d72ccfcfd303 · report

Tasks

Computational EfficiencyGraph LearningGraph RegressionGraph Representation LearningRepresentation Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Graph Property Prediction ogbg-molpcba PDF Ext. data No #8 of 36 Archive leaderboard report
Graph Property Prediction ogbg-molpcba PDF Number of params 3842048 #8 of 36 Archive leaderboard report
Graph Property Prediction ogbg-molpcba PDF Test AP 0.3031 ± 0.0026 #8 of 36 Archive leaderboard report
Graph Property Prediction ogbg-molpcba PDF Validation AP 0.3115 ± 0.0020 #8 of 36 Archive leaderboard report
Graph Regression ZINC PDF MAE 0.066 ± 0.002 #8 of 27 Archive leaderboard report
Graph Regression ZINC-500k PDF MAE 0.066 #9 of 36 Archive leaderboard report

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