Deep Learning on Graphs
I focus on advancing graph neural network (GNN) architectures and improving the expressiveness, scalability, interpretability and robustness of GNNs. More recently, I focus on pre-trained, large-scale foundation models for graph-structured data.
Building on pre-trained GNN models, we explore the general strategy of automatically selecting auxiliary datasets to improve
molecule prediction performance using meta-learning.
DeSCo is the state-of-the-art (pre-trained) GNN that can perform reliable subgraph counting predictions on real-world large networks,
based on symmetry breaking and expressive GNN architecture.
KDD 2023
BatchSampler is a novel and general negative sampling scheme for self-supervised contrastive learning framework. It improves pre-trained model performance in the domains of images, natural language and graphs.
ICML 2022
LA-GNN is a general pre-training framework that improves GNN performance through augmentation.ICLR 2021
GNNs can learn to execute graph algorithms.AAAI 2021
ID-GNN improves the expressiveness of GNN by considering node identities.
NeurIPS 2017
GraphSAGE is a general GNN framework for large-scale graph learning.ICML 2018
GraphRNN is one of the first graph generative models for learning distribution of graphs.NeurIPS 2019
The first framework to explain predictions made by GNNs!
