Kaixiong-Zhou / EGNN
Energetic GraphNeural Networks (EGNN) implementation based on Dirichlet Energy Constrained Learning.
☆25Updated 3 years ago
Alternatives and similar repositories for EGNN:
Users that are interested in EGNN are comparing it to the libraries listed below
- Pytorch implementation of differentiable group normalization (NeurIPS 2020)☆38Updated 4 years ago
- Implementation of the paper "A New Perspective on the Effects of Spectrum in Graph Neural Networks"☆17Updated 2 years ago
- A Note On Over-Smoothing for Graph Neural Network☆20Updated 4 years ago
- The code for the ICML 2021 paper "Graph Neural Networks Inspired by Classical Iterative Algorithms".☆43Updated 3 years ago
- 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
- Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021☆46Updated 3 years ago
- PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"☆87Updated 3 years ago
- Code for paper https://arxiv.org/abs/2102.13186☆44Updated 3 years ago
- [ICLR 2022] Implementation of paper "Automated Self-Supervised Learning for Graphs"☆41Updated 2 years ago
- Papers about developing DL methods on disassortative graphs☆48Updated 2 years ago
- ☆20Updated 2 years ago
- This repository contains the official implementation of the paper "Reliable Graph Neural Networks via Robust Aggregation" (NeurIPS, 2020)…☆17Updated 3 years ago
- Node Dependent Local Smoothing for Scalable Graph Learning (NeurIPS'21, Spotlight)☆21Updated 3 years ago
- [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"☆56Updated last year
- Graph Structured Neural Network☆39Updated 2 years ago
- Open source code for paper "EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks".☆26Updated 2 years ago
- ☆29Updated 3 years ago
- [KDD 2022] Implementation of "Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective"☆45Updated last year
- How Powerful are Spectral Graph Neural Networks☆71Updated last year
- Adaptive evaluation reveals that most examined adversarial defenses for GNNs show no or only marginal improvement in robustness. (NeurIPS…☆29Updated 2 years ago
- Open-source datasets for paper "Fairness in Graph Mining: A Survey".☆17Updated 2 years ago
- PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)☆81Updated 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
- ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily☆43Updated 2 years ago
- ☆17Updated last year
- PyTorch implementation of "Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited"☆35Updated last year
- ☆25Updated 5 years ago
- ☆24Updated 2 years ago
- The official implementation of DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (NeurIPS 2021)☆25Updated 2 years ago
- ☆57Updated 3 years ago