Graph-COM / GSAT
[ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
☆165Updated last year
Alternatives and similar repositories for GSAT:
Users that are interested in GSAT are comparing it to the libraries listed below
- [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs☆108Updated last year
- GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]☆192Updated 3 weeks ago
- The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"☆85Updated 2 years ago
- Adversarial Graph Augmentation to Improve Graph Contrastive Learning☆88Updated 3 years ago
- [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification☆100Updated last year
- [ICML 2022] Local Augmentation for Graph Neural Networks☆66Updated 9 months ago
- Official implementation of AAAI'22 paper "ProtGNN: Towards Self-Explaining Graph Neural Networks"☆50Updated 2 years ago
- Parameterized Explainer for Graph Neural Network☆130Updated last year
- [ICML 2021] "Graph Contrastive Learning Automated" by Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang; [WSDM 2022] "Bringing Yo…☆110Updated 6 months ago
- A collection of papers on Graph Structural Learning (GSL)☆54Updated last year
- Papers about out-of-distribution generalization on graphs.☆166Updated last year
- PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)☆81Updated last year
- [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"☆79Updated 2 years ago
- ☆132Updated last year
- ☆55Updated 2 years ago
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆101Updated 2 years ago
- PyTorch implementation of "BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation"☆53Updated last year
- NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs☆121Updated last year
- Code for paper https://arxiv.org/abs/2102.13186☆44Updated 3 years ago
- Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs☆134Updated 2 years ago
- Papers about developing DL methods on disassortative graphs☆48Updated 2 years ago
- [KDD 2023] What’s Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders☆84Updated 4 months ago
- Official code of "Towards Multi-Grained Explainability for Graph Neural Networks" (NeurIPS 2021) + Pytorch Implementation of recent attri…☆68Updated last month
- [NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods☆122Updated 2 years ago
- A Critical Look at the Evaluation of GNNs under Heterophily: Are We Really Making Progress?☆110Updated 2 years ago
- Ratioanle-aware Graph Contrastive Learning codebase☆40Updated last year
- Official code of "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)☆124Updated last year
- Schedule for learning on graphs seminar☆110Updated last year
- ☆51Updated 2 years ago
- Source code for From Stars to Subgraphs (ICLR 2022)☆67Updated 11 months ago