- Shanghai, China
- https://qitianwu.github.io/
Stars
This is the official implementation of Residual Propagation (RP) proposed in "How Graph Neural Networks Learn: Lessons from Training Dynamics", which is accepted to ICML 2024.
The official implementation for NeurIPS2023 paper "SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations"
Official Implementations of "Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space""
A curated list of resources for OOD detection with graph data.
A comprehensive benchmark of Graph Structure Learning (NeurIPS 2023 Datasets and Benchmarks Track)
New structural distributional shifts for evaluating graph models
PyTorch implementation of GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks
[SIGKDD 2023] HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware Baseline
PyTorch code for Diffusion Mechanism in Neural Network: Theory and Applications
Use ChatGPT to summarize the arXiv papers. 全流程加速科研,利用chatgpt进行论文全文总结+专业翻译+润色+审稿+审稿回复
The official implementation for ICLR23 spotlight paper "DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion"
Official implementation of our ICLR 2023 paper "Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting"
The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"
The official implementation of our paper "MoleRec: Combinatorial Drug Recommendation with Substructure-Aware Molecular Representation Learning" (TheWebConf 2023).
This is the official code repository for "Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs", which is accepted to ICLR 2023.
We would like to maintain a list of resources which aim to solve molecular docking and other closely related tasks.
The official implementation for "Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment" which is accepted to NeurIPS 2022.
The official implementation for "Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks" which is accepted to NeurIPS 2022.
The official implementation of NeurIPS22 spotlight paper "NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification"
A collection of papers on Graph Structural Learning (GSL)
Official implementation for the paper "Learning Substructure Invariance for Out-of-Distribution Molecular Representations" (NeurIPS 2022).
Official implementation for GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs (NeurIPS 2022).
Official PyTorch implementation for the following KDD2022 paper: Variational Inference for Training Graph Neural Networks in Low-Data Regime through Joint Structure-Label Estimation
😎 An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"