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Semi-Supervised Classification with Graph Convolutional Networks (GCN)

Authors: Thomas N. Kipf, Max Welling Year: 2017 (arXiv 2016) Citations: 20,000+

Summary

Graph convolutional networks apply convolutions to graph-structured data. Spectral approximations enable efficient semi-supervised learning on graphs.

Key Concepts

  • Graph Neural Networks: Extend CNNs to non-Euclidean data
  • Spectral Convolutions: Convolutions via spectral theory
  • Localization: Chebyshev polynomials approximate filters
  • Semi-Supervised Learning: Learn from few labeled examples
  • Neighborhood Aggregation: Aggregates from neighbors
  • Scalability: Efficient computation on large graphs

Impact

  • 20,000+ citations
  • Foundation of modern graph neural networks
  • Enabled learning on non-Euclidean data
  • Influenced GraphSAGE, GAT, subsequent GNNs
  • Applications in social, citation, knowledge graphs
  • Opened geometric deep learning research
  • Practical applications in node classification
  • Spectral Networks (Bruna et al., 2013)
  • GraphSAGE (Hamilton et al., 2017)
  • Graph Attention Networks (Veličković et al., 2017)