HongtengXu / Reading-ListLinks
A list of papers for group meeting
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- [ICLR 2023] "Graph Domain Adaptation via Theory-Grounded Spectral Regularization" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen☆23Updated 2 years ago
- ☆11Updated 2 years ago
- [ICLR 2023] Learnable Randomness Injection (LRI) for interpretable Geometric Deep Learning.☆24Updated 2 years ago
- [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs☆116Updated 2 years ago
- Diffusion Models for Graphs Benefit From Discrete State Spaces☆34Updated 2 years ago
- Official implementation for GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs (NeurIPS 20…☆20Updated 2 years ago
- [AAAI2023 Oral] The official implementation of "Hierarchical Contrastive Learning for Temporal Point Processes"☆26Updated 5 months ago
- Official implementation for the paper: Permutation Invariant Graph Generation via Score-Based Generative Modeling☆111Updated 2 years ago
- Learnable Global Pooling Layers Based on Regularized Optimal Transport (ROT)☆16Updated last year
- "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')☆48Updated 3 years ago
- Papers about developing DL methods on disassortative graphs☆48Updated 3 years ago
- Disentangled gEnerative cAusal Representation (DEAR)☆61Updated 2 years ago
- GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]☆201Updated 6 months ago
- A Quasi-Wasserstein Loss for Learning Graph Neural Networks (QW loss)☆11Updated last year
- A curated list of resources for OOD detection with graph data.☆19Updated last year
- Learning Graphons via Structured Gromov-Wasserstein Barycenters☆22Updated 4 years ago
- NeurIPS'22 Oral: EquiVSet - Learning Neural Set Functions Under the Optimal Subset Oracle☆20Updated 2 years ago
- ☆14Updated 3 years ago
- [ICML 2022] pGNN, p-Laplacian Based Graph Neural Networks☆27Updated 3 years ago
- Code for the paper "SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks"☆12Updated 2 years ago
- Official implementation of the ICML2021 paper "Elastic Graph Neural Networks"☆42Updated 4 years ago
- Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching☆44Updated 5 years ago
- ☆16Updated last year
- The code for the ICML 2021 paper "Graph Neural Networks Inspired by Classical Iterative Algorithms".☆43Updated 4 years ago
- [ICML 2023] Structural Re-weighting Improves Graph Domain Adaptation (StruRW)☆21Updated 2 years ago
- Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)☆43Updated 2 years ago
- Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 2021) + Pytorch Implementation of GNN attribution methods☆69Updated 6 months ago
- This repo is for source code of NeurIPS 2021 paper "Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration".☆22Updated 3 years ago
- ☆25Updated 3 years ago
- Size-Invariant Graph Representations for Graph Classification Extrapolations (ICML 2021 Long Talk)☆23Updated 2 years ago