Alternatives and similar repositories for BoT

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

• GemsLab / H2GCN
  Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)
  ☆107Updated 6 months ago
• Shen-Lab / GraphCL_Automated
  [ICML 2021] "Graph Contrastive Learning Automated" by Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang; [WSDM 2022] "Bringing Yo…
  ☆116Updated last year
• nerdslab / bgrl
  PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)
  ☆84Updated 2 years ago
• jianhao2016 / GPRGNN
  ☆139Updated 2 years ago
• CUAI / Non-Homophily-Benchmarks
  [WWW 2021 GLB] New Benchmarks for Learning on Non-Homophilous Graphs
  ☆116Updated 4 years ago
• susheels / adgcl
  Adversarial Graph Augmentation to Improve Graph Contrastive Learning
  ☆92Updated 4 years ago
• mengliu1998 / DL4DisassortativeGraphs
  Papers about developing DL methods on disassortative graphs
  ☆48Updated 3 years ago
• 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 4 years ago
• yzjiao / Subg-Con
  Code for ICDM2020 full paper: "Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning"
  ☆46Updated 3 years ago
• ChandlerBang / DeCorr
  [KDD 2022] Implementation of "Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective"
  ☆45Updated last year
• BorealisAI / SLAPS-GNN
  PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"
  ☆89Updated 4 years ago
• Namkyeong / BGRL_Pytorch
  Pytorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping"
  ☆81Updated 4 years ago
• BUPT-GAMMA / Graph-Structure-Estimation-Neural-Networks
  Source code for WWW 2021 paper "Graph Structure Estimation Neural Networks"
  ☆59Updated 4 years ago
• THUDM / GRAND-plus
  Code and dataset for paper "GRAND+: Scalable Graph Random Neural Networks"
  ☆35Updated 3 years ago
• PKU-DAIR / GAMLP
  Code of GAMLP for Open Graph Benchmark. KDD‘22
  ☆63Updated 3 years ago
• yanghu819 / Graph-MLP
  ☆77Updated 4 years ago
• CUAI / Non-Homophily-Large-Scale
  [NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
  ☆124Updated 3 years ago
• ChandlerBang / GCond
  [ICLR'22][KDD'22][IJCAI'24][NeurIPS'25] Implementation of "Graph Condensation for Graph Neural Networks"
  ☆141Updated 2 months ago
• zwt233 / NDLS
  Node Dependent Local Smoothing for Scalable Graph Learning (NeurIPS'21, Spotlight)
  ☆22Updated 3 years ago
• DM2-ND / CFLP
  Author: Tong Zhao (tzhao2@nd.edu). ICML 2022. Learning from Counterfactual Links for Link Prediction
  ☆71Updated 3 years ago
• jianhao2016 / AllSet
  This is the GitHub repository for our ICLR22 paper: "You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks"
  ☆104Updated 2 years ago
• devnkong / FLAG
  Official implementation of our FLAG paper (CVPR2022)
  ☆145Updated 3 years ago
• lsh0520 / RGCL
  Ratioanle-aware Graph Contrastive Learning codebase
  ☆44Updated 2 years ago
• GraphPKU / JacobiConv
  How Powerful are Spectral Graph Neural Networks
  ☆74Updated 2 years ago
• DongHande / PT_propagation_then_training
  The official pytorch implementation of Propagation_then_Training for graph (https://arxiv.org/abs/2010.12408)
  ☆26Updated 4 years ago
• RecklessRonan / GloGNN
  ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
  ☆47Updated 3 years ago
• junxia97 / SimGRACE
  [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"
  ☆81Updated 3 years ago
• ChandlerBang / GTrans
  [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"
  ☆65Updated 2 years ago
• zpeng27 / GMI
  Graph Representation Learning via Graphical Mutual Information Maximization
  ☆118Updated 5 years ago
• muhanzhang / NestedGNN
  ☆57Updated 4 years ago