Alternatives and similar repositories for EGNN

Users that are interested in EGNN are comparing it to the libraries listed below

• balcilar / gnn-spectral-expressive-power
  Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021
  ☆46Updated 4 years ago
• qslim / gnn-spectrum
  Implementation of the paper "A New Perspective on the Effects of Spectrum in Graph Neural Networks"
  ☆17Updated 3 years ago
• Kaixiong-Zhou / DGN
  Pytorch implementation of differentiable group normalization (NeurIPS 2020)
  ☆38Updated 4 years ago
• BorealisAI / SLAPS-GNN
  PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"
  ☆88Updated 3 years ago
• wokas36 / GraphSNN
  Graph Structured Neural Network
  ☆40Updated 3 years ago
• GraphPKU / JacobiConv
  How Powerful are Spectral Graph Neural Networks
  ☆73Updated 2 years ago
• Chen-Cai-OSU / GNN-Over-Smoothing
  A Note On Over-Smoothing for Graph Neural Network
  ☆20Updated 5 years ago
• GemsLab / H2GCN
  Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)
  ☆105Updated 2 months ago
• yushundong / EDITS
  Open source code for paper "EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks".
  ☆27Updated 3 years ago
• FFTYYY / TWIRLS
  The code for the ICML 2021 paper "Graph Neural Networks Inspired by Classical Iterative Algorithms".
  ☆43Updated 4 years ago
• nerdslab / bgrl
  PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)
  ☆81Updated 2 years ago
• ChandlerBang / GTrans
  [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"
  ☆60Updated 2 years ago
• Graph-COM / Neighborhood-Aware-Temporal-Network
  Yuhong Luo and Pan Li. Neighborhood-aware scalable temporal network representation learning. In Learning on Graphs, 2022.
  ☆27Updated 2 years ago
• lxiaorui / AirGNN
  ☆21Updated 2 years ago
• Yujun-Yan / Heterophily_and_oversmoothing
  Codes for "Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks"
  ☆38Updated 2 years ago
• chr26195 / PMLP
  This is the official code repository for "Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs", wh…
  ☆89Updated last year
• susheels / gnns-and-local-assortativity
  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
• EdisonLeeeee / GreatX
  A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG).
  ☆85Updated 10 months ago
• GemsLab / CPGNN
  ☆29Updated 3 years ago
• ChandlerBang / GCond
  [ICLR'22] [KDD'22] [IJCAI'24] Implementation of "Graph Condensation for Graph Neural Networks"
  ☆140Updated 9 months ago
• qitianwu / GraphOOD-EERM
  The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"
  ☆92Updated 2 years ago
• ChandlerBang / AutoSSL
  [ICLR 2022] Implementation of paper "Automated Self-Supervised Learning for Graphs"
  ☆42Updated 3 years ago
• RManLuo / GSNOP
  Official code implementation for WSDM 23 paper Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs.
  ☆34Updated 2 years ago
• Graph-COM / Neural_Higher-order_Pattern_Prediction
  ☆14Updated 3 years ago
• ChandlerBang / DeCorr
  [KDD 2022] Implementation of "Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective"
  ☆45Updated last year
• CUAI / Non-Homophily-Benchmarks
  [WWW 2021 GLB] New Benchmarks for Learning on Non-Homophilous Graphs
  ☆114Updated 3 years ago
• stadlmax / Graph-Posterior-Network
  Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)
  ☆43Updated 2 years ago
• muhanzhang / NestedGNN
  ☆57Updated 3 years ago
• snap-research / non-contrastive-link-prediction
  [ICLR 2023] Link Prediction with Non-Contrastive Learning
  ☆26Updated 2 years ago
• BUPT-GAMMA / CaGCN
  This repo is for source code of NeurIPS 2021 paper "Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration".
  ☆22Updated 3 years ago