Papers

Early Work

Title	Link	Year
A new model for learning in graph domains	https://ieeexplore.ieee.org/document/1555942	2005
The Graph Neural Network Model	https://ieeexplore.ieee.org/document/4700287	2009

Architectures

Title	Link	Year	Note
Semi-Supervised Classification with Graph Convolutional Networks	https://arxiv.org/abs/1609.02907	2016	GCN
Gated Graph Sequence Neural Networks	https://arxiv.org/abs/1511.05493	2016	GGNN
Inductive Representation Learning on Large Graphs	https://arxiv.org/abs/1706.02216	2017	GraphSAGE
Graph Attention Networks	https://arxiv.org/abs/1710.10903	2018	GAT
How Powerful are Graph Neural Networks?	https://arxiv.org/abs/1810.00826	2018	GIN
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks	https://arxiv.org/abs/1810.02244	2018
Representation Learning on Graphs with Jumping Knowledge Networks	https://arxiv.org/abs/1806.03536	2018
Graph Transformer Networks	https://arxiv.org/abs/1911.06455	2019

Expressivity & Limitations

Title	Link	Year
Relational inductive biases, deep learning, and graph networks	https://arxiv.org/abs/1806.01261	2018
Revisiting Graph Neural Networks: All We Have is Low-Pass Filters	https://arxiv.org/abs/1905.09550	2019
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification	https://arxiv.org/abs/1905.10947	2020
Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View	https://arxiv.org/abs/1909.03211	2020
On the Bottleneck of Graph Neural Networks and its Practical Implications	https://arxiv.org/abs/2006.05205	2021
Understanding over-squashing and bottlenecks on graphs via curvature	https://arxiv.org/abs/2111.14522	2022
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology	https://arxiv.org/abs/2302.02941	2023
WL meet VC	https://arxiv.org/abs/2301.11039	2023
ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond	https://arxiv.org/abs/2303.06562	2023

Graph Rewiring

Title	Link	Year
Diffusion Improves Graph Learning	https://arxiv.org/abs/1911.05485	2019
Understanding over-squashing and bottlenecks on graphs via curvature	https://arxiv.org/abs/2111.14522	2022
DiffWire: Inductive Graph Rewiring via the Lovász Bound	https://arxiv.org/abs/2206.07369	2022
DRew: Dynamically Rewired Message Passing with Delay	https://arxiv.org/abs/2305.08018	2023

Survey

Title	Link	Year
A Comprehensive Survey on Graph Neural Networks	https://arxiv.org/abs/1901.00596	2019
Understanding Pooling in Graph Neural Networks	https://arxiv.org/abs/2110.05292	2022
Attending to Graph Transformers	https://arxiv.org/abs/2302.04181	2023