usail-hkust / Awesome-Causality-Inspired-GNNs
An awesome collection of causality-inspired graph neural networks.
☆59Updated 2 months ago
Alternatives and similar repositories for Awesome-Causality-Inspired-GNNs:
Users that are interested in Awesome-Causality-Inspired-GNNs are comparing it to the libraries listed below
- A collection of papers on Graph Structural Learning (GSL)☆54Updated last year
- [WWW2024 Oral paper] "Graph Out-of-Distribution Generalization via Causal Intervention”.☆22Updated 3 months ago
- ☆16Updated last year
- Code for paper https://arxiv.org/abs/2102.13186☆44Updated 3 years ago
- ☆18Updated 2 years ago
- [KDD 2022] "Causal Attention for Interpretable and Generalizable Graph Classification" by Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, X…☆80Updated last year
- A curated list of papers on graph structure learning (GSL).☆42Updated last month
- The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"☆84Updated 2 years ago
- [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs☆105Updated last year
- PyTorch implementation of "Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited"☆33Updated last year
- Papers about out-of-distribution generalization on graphs.☆164Updated last year
- ☆52Updated 3 months ago
- Open source code for paper "EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks".☆26Updated 2 years ago
- NeurIPS 2022, Revisiting Heterophily For Graph Neural Networks, official PyTorch implementation for Adaptive Channel Mixing (ACM) GNN fra…☆76Updated 2 months ago
- ☆24Updated 2 years ago
- A list of Graph Causal Learning materials.☆188Updated 3 weeks ago
- The official implement of SIGKDD'24 paper: ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs☆24Updated 6 months ago
- ☆99Updated last year
- The code Implementation of the paper “Universal Prompt Tuning for Graph Neural Networks”.☆26Updated last year
- [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.☆163Updated 11 months ago
- Implementation Codes for NeurIPS22 paper "Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift"☆18Updated 2 years ago
- Official implementation of AAAI'22 paper "ProtGNN: Towards Self-Explaining Graph Neural Networks"☆50Updated 2 years ago
- [NeurIPS 2023] "Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift" by Yongduo Sui, Qitian Wu, Jiancan Wu, Q…☆16Updated last year
- PyTorch implementation of "PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters"☆13Updated 10 months ago
- Source code for NeurIPS 2022 paper "Uncovering the Structural Fairness in Graph Contrastive Learning"☆29Updated 2 years ago
- An official PyTorch implementation of "Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels" (WSDM 2022))☆32Updated 2 years ago
- Code for "Graph Structure Learning with Variational Information Bottleneck" published in AAAI 2022☆33Updated 2 years ago
- Open-source Library PyGDebias: Graph Datasets and Fairness-Aware Graph Mining Algorithms☆62Updated 9 months ago
- ☆47Updated 2 years ago
- Published papers focusing on graph domain adaptation☆35Updated 2 months ago