mlsanityData to 14 Oct 2026

Local-to-global Perspectives on Graph Neural Networks

arXiv 2306.06547 ↗First posted 11 Jun 2023cs.LG stat.MLcited by 2

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Abstract (arXiv)

This thesis presents a local-to-global perspective on graph neural networks (GNN), the leading architecture to process graph-structured data. After categorizing GNN into local Message Passing Neural Networks (MPNN) and global Graph transformers, we present three pieces of work: 1) study the convergence property of a type of global GNN, Invariant Graph Networks, 2) connect the local MPNN and global Graph Transformer, and 3) use local MPNN for graph coarsening, a standard subroutine used in global modeling.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 18 of its 49 references that are in the map. First posted on arXiv 11 Jun 2023.

Builds on

18 of 49 references.

References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2018How Powerful are Graph Neural Networks?cited by 4,393
  3. 2018Neural Ordinary Differential Equationscited by 3,779
  4. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,621
  5. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,102
  6. 2018Gradient Descent Provably Optimizes Over-parameterized Neural Networkscited by 659
  7. 2019Provably Powerful Graph Networkscited by 389
  8. 2018Invariant and Equivariant Graph Networkscited by 346
  9. 2020A Note on Over-Smoothing for Graph Neural Networkscited by 249
  10. 2021A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groupscited by 187
  11. 2021Equivariant Subgraph Aggregation Networkscited by 154
  12. 2020Expressive Power of Invariant and Equivariant Graph Neural Networkscited by 100
6 more
  1. 2020Convergence and Stability of Graph Convolutional Networks on Large Random Graphscited by 76
  2. 2020The expressive power of kth-order invariant graph networkscited by 34
  3. 2015Estimating network edge probabilities by neighborhood smoothingcited by 28
  4. 2021On the Universality of Graph Neural Networks on Large Random Graphscited by 26
  5. 2019Incidence Networks for Geometric Deep Learningcited by 17
  6. 2019Graph Neural Networks Exponentially Lose Expressive Power for Node Classificationcited by 16

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Cited by year

Citing papers per year: 2024: 2.

2 papers.

  1. 2024VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identificationcited by 15
  2. 2024Graph neural networks and non-commuting operatorscited by 5