Corpus ID: 231698873

Identity-aware Graph Neural Networks

@article{You2021IdentityawareGN,
  title={Identity-aware Graph Neural Networks},
  author={Jiaxuan You and Jonathan M. Gomes-Selman and Rex Ying and J. Leskovec},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.10320}
}
Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. However, the expressive power of existing GNNs is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test, which means GNNs that are not able to predict node clustering coefficients and shortest path distances, and cannot differentiate between different d-regular graphs. Here we develop a class of message passing GNNs, named Identity-aware Graph Neural Networks (ID-GNNs), with… Expand

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References

SHOWING 1-10 OF 49 REFERENCES
Position-aware Graph Neural Networks
  • 116
  • PDF
How Powerful are Graph Neural Networks?
  • 1,243
  • PDF
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
  • 293
  • PDF
GNNExplainer: Generating Explanations for Graph Neural Networks
  • 124
  • PDF
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning
  • 8
  • PDF
Relational Pooling for Graph Representations
  • 71
  • Highly Influential
  • PDF
Provably Powerful Graph Networks
  • 110
  • Highly Influential
  • PDF
On the equivalence between graph isomorphism testing and function approximation with GNNs
  • 69
  • Highly Influential
  • PDF
Graph Neural Networks for Social Recommendation
  • 211
  • PDF
Residual Gated Graph ConvNets
  • 62
  • PDF
...
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3
4
5
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