sigeisler / reliable_gnn_via_robust_aggregation
This repository contains the official implementation of the paper "Reliable Graph Neural Networks via Robust Aggregation" (NeurIPS, 2020).
☆18Updated 3 years ago
Alternatives and similar repositories for reliable_gnn_via_robust_aggregation
Users that are interested in reliable_gnn_via_robust_aggregation are comparing it to the libraries listed below
Sorting:
- Adaptive evaluation reveals that most examined adversarial defenses for GNNs show no or only marginal improvement in robustness. (NeurIPS…☆29Updated 2 years ago
- This repository contains the official implementation of the paper "Robustness of Graph Neural Networks at Scale" (NeurIPS, 2021).☆30Updated last year
- How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications (KDD'22)☆13Updated 2 years ago
- Adversarial Graph Augmentation to Improve Graph Contrastive Learning☆88Updated 3 years ago
- Imbalanced Network Embedding vi aGenerative Adversarial Graph Networks☆27Updated 3 years ago
- ☆20Updated 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
- Energetic GraphNeural Networks (EGNN) implementation based on Dirichlet Energy Constrained Learning.☆25Updated 3 years ago
- ☆29Updated 3 years ago
- Papers about developing DL methods on disassortative graphs☆48Updated 2 years ago
- Pytorch implementation of gnn meta attack (mettack). Paper title: Adversarial Attacks on Graph Neural Networks via Meta Learning.☆21Updated 4 years ago
- How Powerful are Spectral Graph Neural Networks☆72Updated last year
- A Note On Over-Smoothing for Graph Neural Network☆20Updated 4 years ago
- ☆134Updated last year
- This repo contains a reference implementation for the paper "Breaking the Limit of Graph Neural Networks by Improving the Assortativity o…☆32Updated 3 years ago
- Open source code for paper "EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks".☆26Updated 2 years ago
- Code for paper https://arxiv.org/abs/2102.13186☆44Updated 4 years ago
- [ICLR 2022] Implementation of paper "Automated Self-Supervised Learning for Graphs"☆42Updated 2 years ago
- [NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods☆122Updated 2 years ago
- Codes for "Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks"☆38Updated 2 years ago
- ☆55Updated 3 years ago
- [WWW 2021 GLB] New Benchmarks for Learning on Non-Homophilous Graphs☆112Updated 3 years ago
- Defending graph neural networks against adversarial attacks (NeurIPS 2020)☆64Updated last year
- Graph Structured Neural Network☆40Updated 2 years ago
- ☆57Updated 3 years ago
- A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG).☆85Updated 7 months ago
- Pytorch implementation of differentiable group normalization (NeurIPS 2020)☆38Updated 4 years ago
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆101Updated 3 years ago
- Code for ICDM2020 full paper: "Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning"☆45Updated 3 years ago
- The code for the ICML 2021 paper "Graph Neural Networks Inspired by Classical Iterative Algorithms".☆43Updated 3 years ago