A graph convolutional network model is proposed for semi-supervised learning that takes into account both the graph structure and node features. The model uses a graph convolutional layer that approximates spectral graph convolutions using a localized first-order approximation. This allows the model to be applied to large-scale problems. The model is evaluated on several benchmark semi-supervised classification datasets where it achieves state-of-the-art performance.

1. SEMI-SUPERVISED CLASSIFICATION
WITH GRAPH CONVOLUTIONAL
NETWORKS

2. Schematic depiction of multi-layer Graph Convolutional Network (GCN) for
semisupervised learning with C input channels and F feature maps in the output
layer. The graph structure (edges shown as black lines) is shared over layers, labels
are denoted by Y

3. A neural network model for graph-structured data should take both the graph structure and
feature description of nodes into account
Fast Approximate Convolution on Graphs:

4. D D-AA
Symmetric normalized Laplacian:
Combinatorial Laplacian:
Graph structure

5. Loss function:

6. experiments

7. Appendix:
Proof:
1.

8. 2.

9. 3.