Graph-Learning-Benchmarks / gli
π Graph Learning Indexer: a contributor-friendly and metadata-rich platform for graph learning benchmarks. Dataloading, Benchmarking, Tagging, and more!
β42Updated 8 months ago
Related projects β
Alternatives and complementary repositories for gli
- [ICLR 2023] MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initializationβ76Updated last year
- This is an authors' implementation of the NIPS 2022 dataset and Benchmark Track Paper "A Comprehensive Study on Large Scale Graph Traininβ¦β62Updated last year
- [ICML 2021] "A Unified Lottery Tickets Hypothesis for Graph Neural Networks", Tianlong Chen*, Yongduo Sui*, Xuxi Chen, Aston Zhang, Zhangβ¦β63Updated last year
- This repository contains the official implementation of the paper "Robustness of Graph Neural Networks at Scale" (NeurIPS, 2021).β28Updated last year
- Official implementation of our VQ-GNN paper (NeurIPS2021)β33Updated 3 years ago
- Defending graph neural networks against adversarial attacks (NeurIPS 2020)β58Updated last year
- "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')β47Updated 2 years ago
- [NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodsβ118Updated 2 years ago
- β18Updated 2 years ago
- The code for the ICML 2021 paper "Graph Neural Networks Inspired by Classical Iterative Algorithms".β43Updated 3 years ago
- β24Updated 2 months ago
- PyTorch implementation of GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networksβ35Updated last year
- β54Updated 3 years ago
- SIGN: Scalable Inception Graph Networkβ94Updated 4 years ago
- [WWW 2021 GLB] New Benchmarks for Learning on Non-Homophilous Graphsβ111Updated 2 years ago
- β77Updated last year
- [ICLR 2022] Code for Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation (GLNN)β86Updated 2 weeks ago
- [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"β53Updated last year
- Graph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for evaluating the adversarial robustness of Graph Mβ¦β91Updated last year
- [ICLR 2024] VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPsβ78Updated 7 months ago
- Adaptive evaluation reveals that most examined adversarial defenses for GNNs show no or only marginal improvement in robustness. (NeurIPSβ¦β28Updated last year
- Rex Ying's Ph.D. Thesis, Stanford Universityβ40Updated 2 years ago
- A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which appeared in β¦β29Updated 2 years ago
- Scalable Graph Neural Networks for Heterogeneous Graphsβ72Updated 3 years ago
- PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)β80Updated last year
- "Do We Need Anisotropic Graph Neural Networks?" at ICLR 2022β32Updated 2 years ago
- [ICLR 2023] Link Prediction with Non-Contrastive Learningβ25Updated last year
- [ICLR'22] [KDD'22] [IJCAI'24] Implementation of "Graph Condensation for Graph Neural Networks"β125Updated last week
- β37Updated last year
- SCR: Training Graph Neural Networks with Consistency Regularizationβ37Updated 2 years ago