mlsanityData to 14 Oct 2026

Two-level Graph Neural Network

arXiv 2201.01190 ↗First posted 3 Jan 2022cs.LG cs.AIcited by 4

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Abstract (arXiv)

Graph Neural Networks (GNNs) are recently proposed neural network structures for the processing of graph-structured data. Due to their employed neighbor aggregation strategy, existing GNNs focus on capturing node-level information and neglect high-level information. Existing GNNs therefore suffer from representational limitations caused by the Local Permutation Invariance (LPI) problem. To overcome these limitations and enrich the features captured by GNNs, we propose a novel GNN framework, referred to as the Two-level GNN (TL-GNN). This merges subgraph-level information with node-level information. Moreover, we provide a mathematical analysis of the LPI problem which demonstrates that subgraph-level information is beneficial to overcoming the problems associated with LPI. A subgraph counting method based on the dynamic programming algorithm is also proposed, and this has time complexity is O(n^3), n is the number of nodes of a graph. Experiments show that TL-GNN outperforms existing GNNs and achieves state-of-the-art performance.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 17 of its 42 references that are in the map. First posted on arXiv 3 Jan 2022.

Builds on

17 of 42 references.

References · 17

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2018How Powerful are Graph Neural Networks?cited by 4,393
  5. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  6. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 754
  7. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 737
  8. 2020Directional Message Passing for Molecular Graphscited by 561
  9. 20183D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Datacited by 354
  10. 2020Generalization and Representational Limits of Graph Neural Networkscited by 218
  11. 2020Subgraph Neural Networkscited by 73
  12. 2019Approximation Ratios of Graph Neural Networks for Combinatorial Problemscited by 69
5 more
  1. 2019Learning metrics for persistence-based summaries and applications for graph classificationcited by 57
  2. 2020Ripple Walk Training: A Subgraph-based training framework for Large and Deep Graph Neural Networkcited by 19
  3. 2019edGNN: a Simple and Powerful GNN for Directed Labeled Graphscited by 18
  4. 2013Subgraph Frequencies: Mapping the Empirical and Extremal Geography of Large Graph Collectionscited by 8
  5. 2020Ring Reservoir Neural Networks for Graphscited by 3

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Cited by year

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

4 papers.

  1. 2023Graph Neural Networks in Vision-Language Image Understanding: A Surveycited by 10
  2. 2025Addressing Noise and Stochasticity in Fraud Detection for Service Networkscited by 2
  3. 2024PromptGCN: Bridging Subgraph Gaps in Lightweight GCNscited by 1
  4. 2024Grothendieck Graph Neural Networks Framework: An Algebraic Platform for Crafting Topology-Aware GNNscited by 1