Presented by: Xavier Bresson (Swiss Federal Institute of Technology) – Fast Convolutional Neural Networks for Graph-Structured Data
Why CNNs work?
Local stationary points.
CNN for Graph Structured Data
- Graph -> Euclidean Grid
- Graph coarse -> Downsampling(pooling)
Related work
Categories of graph CNNs
- Spatial approach
- Spectral(Fourier) approach
Convolution on Graph
- Graph Laplace
- Fourier transform on graph
- Localized Filters
Fast Chebyshev Polynomial Kernels
Graph Coarsening
- Graph partitioning: Balance Cut/Graclus
- Fast Graph Pooling
Optimization
- Backpropagation
- Gradient Descent
Numeric Computation
- Tensorflow
- CUDA k40 (GPU x8 faster than CPU)
Result
- Euclidean CNNs
- Non-Euclidean CNNs
Future
- Social networks
- Gene networks
- etc.
