[Paper Review] Summaries on a Collection of Papers on Graph Neural Networks (GraphSAGE, GAT, NRI, Neural ODE, CGNN, SGC, GRAND, GGNN, GRAFF)

2023, Aug 16    

Papers

• Summary : GraphSAGE
• Paper : Inductive Representation Learning on Large Graphs, Hamilton et al, 2017

• Summary : GAT
• Paper : Graph Attention Networks, Veličković et al, 2018

• Summary : VAE + NRI
• Paper : Neural Relational Inference for Interacting Systems, Kipf et al, 2018

• Summary : SGC
• Paper : Simplifying Graph Convolutional Networks, Wu et al, 2019

• Summary : Neural ODE
• Paper : Neural Ordinary Differential Equations, Chen et al, 2019

• Summary : PageRank + CGNN
• Paper : Continuous Graph Neural Networks, Xhonneux et al, 2020

• Summary : GRAND
• Paper : GRAND: Graph Neural Diffusion, Chamberlain et al, 2021

• Summary : GPR-GNN
• Paper : Adaptive Universal Generalized PageRank Graph Neural Network, Chien et al, 2021

• Summary : GGNN
• Paper : Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks, Yan et al, 2022

• Summary : GRAFF
• Paper : Understanding convolution on graphs via energies, Giovanni et al, 2023