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SEMI-SUPERVISED CLASSIFICATION
WITH GRAPH CONVOLUTIONAL
NETWORKS
Schematic depiction of multi-layer Graph Convolutional Network (GCN) for
semisupervised learning with C input channels and...
A neural network model for graph-structured data should take both the graph structure and
feature description of nodes int...
D D-AA
Symmetric normalized Laplacian:
Combinatorial Laplacian:
Graph structure
Loss function:
experiments
Appendix:
Proof:
1.
2.
3.
Semi supervised classification with graph convolutional networks
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Semi supervised classification with graph convolutional networks Slide 1 Semi supervised classification with graph convolutional networks Slide 2 Semi supervised classification with graph convolutional networks Slide 3 Semi supervised classification with graph convolutional networks Slide 4 Semi supervised classification with graph convolutional networks Slide 5 Semi supervised classification with graph convolutional networks Slide 6 Semi supervised classification with graph convolutional networks Slide 7 Semi supervised classification with graph convolutional networks Slide 8 Semi supervised classification with graph convolutional networks Slide 9 Semi supervised classification with graph convolutional networks Slide 10
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Semi supervised classification with graph convolutional networks

  1. 1. SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
  2. 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. 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. 4. D D-AA Symmetric normalized Laplacian: Combinatorial Laplacian: Graph structure
  5. 5. Loss function:
  6. 6. experiments
  7. 7. Appendix: Proof: 1.
  8. 8. 2.
  9. 9. 3.

yuhang ding

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