Primers • Graph Neural Networks
 Background: Graph Neural Networks
 Gentle Introduction to GNNs
 Survey Papers on GNNs
 Diving Deep into GNNs
 GNN Papers and Implementations
 Benchmarks and Datasets
 Tools
 Citation
Background: Graph Neural Networks
 Graph neural networks (GNNs) are rapidly advancing progress in ML for complex graph data applications. I’ve composed this concise recipe dedicated to students who are lookin to learn and keep uptodate with GNNs. It’s nonexhaustive but it aims to get students familiar with the topic.
Gentle Introduction to GNNs

There are several introductory content to learn about GNNs. The following are some useful ones:
 Foundations of GNNs (by Petar Veličković)
 Gentle Introduction to GNNs (by Distill)
 Understanding Convolutions on Graphs (by Distill)
 Graph Convolutional Networks (by Thomas Kipf)
Survey Papers on GNNs
 Here are two fantastic survey papers on the topic to get a broader and concise picture of GNNs and recent progress:
 Graph Neural Networks: Methods, Applications, and Opportunities (by Lilapati Waikhom and Ripon Patgiri)
 [A] Comprehensive Survey on Graph Neural Networks](https://arxiv.org/abs/1901.00596) (by Zonghan Wu et al.)
Diving Deep into GNNs
 After going through quick highlevel introductory content, here are some great material to go deep:
 Geometric Deep Learning (by Michael Bronstein et al.)
 Graph Representation Learning Book (by William Hamilton)
 CS224W: ML with Graphs (by Jure Leskovec)
GNN Papers and Implementations
 If you want to keep uptodate with popular recent methods and paper implementations for GNNs, the Papers with Code community maintains this useful collection:
Benchmarks and Datasets
 If you are interested in benchmarks/leaderboards and graph datasets that evaluate GNNs, the Papers with Code community also maintains such content here:
Tools
 Here are a few useful tools to get started with GNNs:
Citation
If you found our work useful, please cite it as:
@article{Chadha2020DistilledGraphNeuralNetworks,
title = {Graph Neural Networks},
author = {Chadha, Aman},
journal = {Distilled AI},
year = {2020},
note = {\url{https://aman.ai}}
}