GNN Basics I  Deep Learning Advances on Graphs
25 Jan 2019 2Graphs 0Basics 8Scalable invariant scalable embeddingPresenter  Papers  Paper URL  Our Notes 

Basics  GraphSAGE: Largescale Graph Representation Learning by Jure Leskovec Stanford University  URL + PDF  
Basics  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering by Xavier Bresson  URL + PDF  Ryan Pdf 
Basics  Gated Graph Sequence Neural Networks by Microsoft Research  URL + PDF  Faizan Pdf 
Basics  DeepWalk  Turning Graphs into Features via Network Embeddings  URL + PDF  
Basics  Spectral Networks and Locally Connected Networks on Graphs ^{1}  GaoJi slides + Bill Pdf  
Basics  A Comprehensive Survey on Graph Neural Networks/ Graph Neural Networks: A Review of Methods and Applications  Jack Pdf  
GCN  SemiSupervised Classification with Graph Convolutional Networks  Jack Pdf 

_{ Some Relevant Notes from URL. On periodic domain, people always use Fourier basis, which eigenvectors of Laplace operator. On sphere, people use spherical harmonics, which also are eigenvectors of Laplace operator. In applied science, people decompose functions on a graph using eigenvectors of graph laplacian. Why are these basis preferred? The exponentials used in Fourier series are eigenvalues of shifts, and thus of any operator commuting with shifts, not just Laplacian. Similarly, spherical harmonics carry irreducible representations of 𝑆𝑂(3) and so they are eigenfunctions of any rotationally invariant operator. If the underlying space has symmetries, it’s no wonder that a basis respecting those symmetries has some nice properties. } ↩