2020/05/25 Presenter: Eunbyeol Cho

2. Introduction
Graph Convolutional Network

3. Introduction
출처 : (Fey et al., CVPR, 2018)

4. Introduction

5. Graph
Graph

6. Graph
Image to graph

7. Graph

8. Graph Convolutional Network
Graph Convolutional Network

9. - Preserve the spatial structure
- Weight sharing
Graph Convolutional Network

10. Adjacency matrix
Node feature
DNN
Ideal algorithm?
[ Naïve Approach ]
1. # of parameters
2. Invariant to node ordering
3. Locality
Graph Convolutional Network

11. Graph Convolutional Network

12. Purpose : graph G=(V,E)에 대해서
Graph-based NN model z = f (X, A) 에서 f 학습하기
Graph Convolutional Network

13. [ Input ]
[ Output ]
[ NN layer]
Graph Convolutional Network

14. [ Layer-wise propagation rule ]
Graph Convolutional Network
With weight matix W(l) of dimension F(l) x F(l+1)

15. [ Layer-wise propagation rule ]
Graph Convolutional Network
symmetric normalized Laplacian matrix form

16. [ Convolution on Graphs? ]
Chebyshev polynomial
Eigen value, vector,
decomposition
Fourier transform
Normalized Graph Laplacian
…….
Graph Convolutional Network
[ Fast Approximate Convolutions on Graphs]
= [ Graph Fourier Transform from Laplacian Matrix ]

17. Graph Convolutional Network
https://raw.githubusercontent.com/wiki/alibaba/euler/images/GCN.png

19. • Kipf, Thomas N., and Max Welling. "Semi-supervised classification with graph convolutional
networks." arXiv preprint arXiv:1609.02907 (2016).