4.5k citations · 11.2k across the 85 of their papers we have counts for
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Design Space for Graph Neural Networks
Jiaxuan You, Rex Ying, Jure Leskovec
The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new architectures as well as novel applications. However, current research focuses on proposing a…
Multi-hop Attention Graph Neural Network
Guangtao Wang, Rex Ying, Jing Huang +1
Self-attention mechanism in graph neural networks (GNNs) led to state-of-the-art performance on many graph representation learning tasks. Currently, at every layer, attention is co…
Neural Subgraph Matching
Rex, Ying, Zhaoyu Lou +4
Subgraph matching is the problem of determining the presence and location(s) of a given query graph in a large target graph. Despite being an NP-complete problem, the subgraph matc…
Learning to Simulate Complex Physics with Graph Networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff +3
Here we present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and…