# GNN Basics I - Deep Learning Advances on Graphs

Presenter Papers Paper URL Our Notes
Basics GraphSAGE: Large-scale 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 Pdf GaoJi slides + Bill Pdf
Basics A Comprehensive Survey on Graph Neural Networks/ Graph Neural Networks: A Review of Methods and Applications Pdf Jack Pdf
GCN Semi-Supervised Classification with Graph Convolutional Networks Pdf Jack Pdf
1. 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.

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