776 citations · 5k across the 72 of their papers we have counts for
12 papers · 1 filter
Flaky Performances when Pretraining on Relational Databases
Shengchao Liu, David Vazquez, Jian Tang +1
We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs extracted from relational databases (RDBs). I…
Inductive Logical Query Answering in Knowledge Graphs
Mikhail Galkin, Zhaocheng Zhu, Hongyu Ren +1
Formulating and answering logical queries is a standard communication interface for knowledge graphs (KGs). Alleviating the notorious incompleteness of real-world KGs, neural metho…
Learning on Large-scale Text-attributed Graphs via Variational Inference
Jianan Zhao, Meng Qu, Chaozhuo Li +5
This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. An ideal solution for such a problem would be integrating both…
Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure
Shaohua Fan, Xiao Wang, Yanhu Mo +2
Most Graph Neural Networks (GNNs) predict the labels of unseen graphs by learning the correlation between the input graphs and labels. However, by presenting a graph classification…
Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching
Shengchao Liu, Hongyu Guo, Jian Tang
Molecular representation pretraining is critical in various applications for drug and material discovery due to the limited number of labeled molecules, and most existing work focu…
Evaluating Self-Supervised Learning for Molecular Graph Embeddings
Hanchen Wang, Jean Kaddour, Shengchao Liu +3
Graph Self-Supervised Learning (GSSL) provides a robust pathway for acquiring embeddings without expert labelling, a capability that carries profound implications for molecular gra…
An Empirical Study of Retrieval-enhanced Graph Neural Networks
Dingmin Wang, Shengchao Liu, Hanchen Wang +5
Graph Neural Networks (GNNs) are effective tools for graph representation learning. Most GNNs rely on a recursive neighborhood aggregation scheme, named message passing, thereby th…
High-Order Pooling for Graph Neural Networks with Tensor Decomposition
Chenqing Hua, Guillaume Rabusseau, Jian Tang
Graph Neural Networks (GNNs) are attracting growing attention due to their effectiveness and flexibility in modeling a variety of graph-structured data. Exiting GNN architectures u…
Neural Structured Prediction for Inductive Node Classification
Meng Qu, Huiyu Cai, Jian Tang
This paper studies node classification in the inductive setting, i.e., aiming to learn a model on labeled training graphs and generalize it to infer node labels on unlabeled test g…
GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation
Minkai Xu, Lantao Yu, Yang Song +3
Predicting molecular conformations from molecular graphs is a fundamental problem in cheminformatics and drug discovery. Recently, significant progress has been achieved with machi…
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering
Jing Zhang, Xiaokang Zhang, Jifan Yu +4
Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning. A desired subgraph is crucial as a small one may exclude the answer but a large on…
Generative Coarse-Graining of Molecular Conformations
Wujie Wang, Minkai Xu, Chen Cai +5
Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drastically accelerates simulation. However, s…