mlsanityData to 7 Oct 2026

Simple Graph Convolutional Networks

arXiv 2106.05809 ↗First posted 10 Jun 2021cs.LGcited by 2

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Abstract (arXiv)

Many neural networks for graphs are based on the graph convolution operator, proposed more than a decade ago. Since then, many alternative definitions have been proposed, that tend to add complexity (and non-linearity) to the model. In this paper, we follow the opposite direction by proposing simple graph convolution operators, that can be implemented in single-layer graph convolutional networks. We show that our convolution operators are more theoretically grounded than many proposals in literature, and exhibit state-of-the-art predictive performance on the considered benchmark datasets.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 18 of its 31 references that are in the map. First posted on arXiv 10 Jun 2021.

Builds on

18 of 31 references.

References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2018How Powerful are Graph Neural Networks?cited by 4,355
  4. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  5. 2019Simplifying Graph Convolutional Networkscited by 1,470
  6. 2018Deep Graph Infomaxcited by 1,101
  7. 2016HyperNetworkscited by 1,003
  8. 2015Gated Graph Sequence Neural Networkscited by 792
  9. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  10. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 583
  11. 2016Dynamic Filter Networkscited by 318
  12. 2016Learning Convolutional Neural Networks for Graphscited by 314
6 more
  1. 2017Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphscited by 307
  2. 2015Diffusion-Convolutional Neural Networkscited by 268
  3. 2019Graph Neural Networks with convolutional ARMA filterscited by 185
  4. 2019LanczosNet: Multi-Scale Deep Graph Convolutional Networkscited by 73
  5. 2014A Stochastic Quasi-Newton Method for Large-Scale Optimizationcited by 68
  6. 2018On Filter Size in Graph Convolutional Networkscited by 3

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Cited by year

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

2 papers.

  1. 2023Graph Neural Networks for Text Classification: A Surveycited by 15
  2. 2024Graph Neural Networks on Discriminative Graphs of Wordscited by 0