mlsanityData to 14 Oct 2026

High-Order Pooling for Graph Neural Networks with Tensor Decomposition

arXiv 2205.11691 ↗First posted 24 May 2022cs.LG cs.AIcited by 24

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Abstract (arXiv)

Graph Neural Networks (GNNs) are attracting growing attention due to their effectiveness and flexibility in modeling a variety of graph-structured data. Exiting GNN architectures usually adopt simple pooling operations (eg. sum, average, max) when aggregating messages from a local neighborhood for updating node representation or pooling node representations from the entire graph to compute the graph representation. Though simple and effective, these linear operations do not model high-order non-linear interactions among nodes. We propose the Tensorized Graph Neural Network (tGNN), a highly expressive GNN architecture relying on tensor decomposition to model high-order non-linear node interactions. tGNN leverages the symmetric CP decomposition to efficiently parameterize permutation-invariant multilinear maps for modeling node interactions. Theoretical and empirical analysis on both node and graph classification tasks show the superiority of tGNN over competitive baselines. In particular, tGNN achieves the most solid results on two OGB node classification datasets and one OGB graph classification dataset.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). Builds on 40 of its 59 references that are in the map. First posted on arXiv 24 May 2022.

Builds on

40 of 59 references.

References · 40

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28 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 6; 2024: 10; 2025: 4; 2026: 3.

24 papers.

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  1. 2024Graph Mamba: Towards Learning on Graphs with State Space Modelscited by 86
  2. 2024Polynormer: Polynomial-Expressive Graph Transformer in Linear Timecited by 65
  3. 2023MUDiff: Unified Diffusion for Complete Molecule Generationcited by 42
  4. 2023Sheaf Hypergraph Networkscited by 34
  5. 2023Tensor Networks Meet Neural Networks: A Survey and Future Perspectivescited by 32
  6. 2023Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detectioncited by 19
  7. 2024ReactZyme: A Benchmark for Enzyme-Reaction Predictioncited by 13
  8. 2022On the Ability of Graph Neural Networks to Model Interactions Between Verticescited by 12
  9. 2024Heterogeneous Sheaf Neural Networkscited by 10
  10. 2023When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishabilitycited by 7
  11. 2024Best of Both Worlds: Advantages of Hybrid Graph Sequence Modelscited by 6
  12. 2024Effective Protein-Protein Interaction Exploration with PPIretrievalcited by 5
12 more
  1. 2024EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamicscited by 5
  2. 2024Tensor Network-Constrained Kernel Machines as Gaussian Processescited by 3
  3. 2025Hypergraph Neural Sheaf Diffusion: A Symmetric Simplicial Set Framework for Higher-Order Learningcited by 3
  4. 2025GraphTARIF: Linear Graph Transformer with Augmented Rank and Improved Focuscited by 2
  5. 2023Tensorized Hypergraph Neural Networkscited by 1
  6. 2025Multi-view Graph Condensation via Tensor Decompositioncited by 1
  7. 2024Lying Graph Convolution: Learning to Lie for Node Classification Taskscited by 0
  8. 2024Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learningcited by 0
  9. 2025Structure-Preserving Margin Distribution Learning for High-Order Tensor Data with Low-Rank Decompositioncited by 0
  10. 2026Neighbourhood Transformer: Switchable Attention for Monophily-Aware Graph Learningcited by 0
  11. 2026Fast Tensorization of Neural Networks via Slice-wise Feature Distillationcited by 0
  12. 2026Revisiting Pre-Propagation GNNs: Robust Diffusion Operators and Hidden-State Re-Propagationcited by 0