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Paper ID: 72


  • Graphs
  • Convolutional Networks
  • Semi-supervised
  • Learning Representations

Abstract: We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks that operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.

Link to Paper: https://arxiv.org/pdf/1609.02907.pdf

Conference: ICLR 2017

For an introduction to GCNs-: http://tkipf.github.io/graph-convolutional-networks/


1)Implement the GCN Model on dataset-: Pubmed and Cora