DongHande / PT_propagation_then_trainingLinks
The official pytorch implementation of Propagation_then_Training for graph (https://arxiv.org/abs/2010.12408)
☆26Updated 4 years ago
Alternatives and similar repositories for PT_propagation_then_training
Users that are interested in PT_propagation_then_training are comparing it to the libraries listed below
Sorting:
- Source code for WWW 2021 paper "Graph Structure Estimation Neural Networks"☆59Updated 4 years ago
- [ICLR 2022] Implementation of paper "Automated Self-Supervised Learning for Graphs"☆42Updated 3 years ago
- Code for paper "Mixup for Node and Graph Classification", WWW 2021☆47Updated 4 years ago
- ☆139Updated 2 years ago
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆107Updated 6 months ago
- Code for KDD'22 paper, COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning☆49Updated 2 years ago
- PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)☆84Updated 2 years ago
- [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"☆65Updated 2 years ago
- [ICML 2021] "Graph Contrastive Learning Automated" by Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang; [WSDM 2022] "Bringing Yo…☆116Updated last year
- Node Dependent Local Smoothing for Scalable Graph Learning (NeurIPS'21, Spotlight)☆22Updated 3 years ago
- ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily☆46Updated 3 years ago
- [WWW 2021 GLB] New Benchmarks for Learning on Non-Homophilous Graphs☆116Updated 4 years ago
- [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"☆81Updated 3 years ago
- Code and dataset for paper "GRAND+: Scalable Graph Random Neural Networks"☆35Updated 3 years ago
- PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"☆89Updated 4 years ago
- Implementation for Simple Spectral Graph Convolution in ICLR 2021☆85Updated 3 years ago
- Adversarial Graph Augmentation to Improve Graph Contrastive Learning☆92Updated 4 years ago
- The official code of WWW2021 paper: Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation …☆77Updated 4 years ago
- Code for The Web Conference 2022 Paper "Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding"☆17Updated 3 years ago
- Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs☆138Updated 3 years ago
- Codes for 'From Canonical Correlation Analysis to Self-supervised Graph Neural Networks'. https://arxiv.org/abs/2106.12484☆70Updated 2 years ago
- Official repository for ICLR'23 paper: Multi-task Self-supervised Graph Neural Network Enable Stronger Task Generalization☆39Updated 2 years ago
- Author: Tong Zhao (tzhao2@nd.edu). ICML 2022. Learning from Counterfactual Links for Link Prediction☆71Updated 3 years ago
- ☆57Updated 2 years ago
- [KDD 2022] Implementation of "Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective"☆45Updated 2 years ago
- Graph Representation Learning via Graphical Mutual Information Maximization☆118Updated 5 years ago
- The official source code for "Augmentation-Free Self-Supervised Learning on Graphs"☆77Updated 3 years ago
- ☆77Updated 4 years ago
- Pytorch implementation of differentiable group normalization (NeurIPS 2020)☆38Updated 5 years ago
- The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"☆92Updated 3 years ago