Radical3-HeZhang / Awesome-Trustworthy-GNNs
☆99Updated 9 months ago
Alternatives and similar repositories for Awesome-Trustworthy-GNNs:
Users that are interested in Awesome-Trustworthy-GNNs are comparing it to the libraries listed below
- A curated list of publications and code about data augmentaion for graphs.☆64Updated 2 years ago
- The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"☆87Updated 2 years ago
- ☆24Updated 2 years ago
- Learning to Drop: Robust Graph Neural Network via Topological Denoising & Robust Graph Representation Learning via Neural Sparsification☆78Updated 3 years ago
- Codes and datasets for AAAI-2021 paper "Learning to Pre-train Graph Neural Networks"☆89Updated 4 years ago
- Source code for WWW 2021 paper "Graph Structure Estimation Neural Networks"☆58Updated 3 years ago
- Adversarial Graph Augmentation to Improve Graph Contrastive Learning☆88Updated 3 years ago
- G-NIA model from "Single Node Injection Attack against Graph Neural Networks" (CIKM 2021)☆30Updated 3 years ago
- Schedule for learning on graphs seminar☆109Updated last year
- [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"☆79Updated 2 years ago
- Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs☆134Updated 2 years ago
- ☆76Updated 3 years ago
- How Powerful are Spectral Graph Neural Networks☆72Updated last year
- Parameterized Explainer for Graph Neural Network☆132Updated last year
- The official code of WWW2021 paper: Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation …☆76Updated 3 years ago
- Defending graph neural networks against adversarial attacks (NeurIPS 2020)☆64Updated last year
- This is the official code repository for "Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs", wh…☆88Updated 10 months ago
- A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG).☆85Updated 6 months ago
- [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification☆103Updated last year
- Codes for 'From Canonical Correlation Analysis to Self-supervised Graph Neural Networks'. https://arxiv.org/abs/2106.12484☆68Updated last year
- Code for NeurIPS 2022 paper "Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discriminat…☆55Updated 2 years ago
- TNNLS: A Synergistic Approach for Graph Anomaly Detection with Pattern Mining and Feature Learning; CIKM'20: Error-bounded Graph Anomaly …☆41Updated last year
- Code for paper https://arxiv.org/abs/2102.13186☆44Updated 4 years ago
- A collection of graph data used for semi-supervised node classification.☆39Updated 2 years ago
- An official PyTorch implementation of "Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels" (WSDM 2022))☆33Updated 2 years ago
- A curated list of papers and code related to class-imbalanced learning on graphs (CILG).☆38Updated 3 months ago
- Code for paper "Mixup for Node and Graph Classification", WWW 2021☆47Updated 3 years ago
- Code for Neurips2021 Paper "Topology-Imbalance Learning for Semi-Supervised Node Classification".☆55Updated 3 years ago
- DGL implementation of GRAND(Graph Random Neural Network, NeurIPS 2020)☆18Updated 4 years ago
- Code for "Graph Structure Learning with Variational Information Bottleneck" published in AAAI 2022☆35Updated 2 years ago