TianjinYellow / UGTs-LoG
This is the official code for UGTs.
☆13Updated 2 years ago
Alternatives and similar repositories for UGTs-LoG:
Users that are interested in UGTs-LoG are comparing it to the libraries listed below
- The code for the ICML 2021 paper "Graph Neural Networks Inspired by Classical Iterative Algorithms".☆43Updated 3 years ago
- [ICLR 2023 notable top-5%] Rethinking the Expressive Power of GNNs via Graph Biconnectivity (official implementation)☆104Updated last year
- the code of MoG☆14Updated 8 months ago
- Implementation Codes for ICLR 2023 paper "AutoGT: Automated Graph Transformer Architecture Search"☆2Updated last year
- [ICLR 2022] Code for Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation (GLNN)☆88Updated 5 months ago
- This is an authors' implementation of the NIPS 2022 dataset and Benchmark Track Paper "A Comprehensive Study on Large Scale Graph Trainin…☆65Updated 2 years ago
- [ECCV'22] Equivariant Hypergraph Neural Networks, in PyTorch☆30Updated 2 years ago
- Some thoughts about writing scientific papers☆18Updated 5 months ago
- Official PyTorch implementation for the following KDD2022 paper: Variational Inference for Training Graph Neural Networks in Low-Data Re…☆19Updated 2 years ago
- Official implementation of our VQ-GNN paper (NeurIPS2021)☆38Updated 3 years ago
- [ICLR 2023] MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization☆77Updated 2 years ago
- [ICML 2021] "A Unified Lottery Tickets Hypothesis for Graph Neural Networks", Tianlong Chen*, Yongduo Sui*, Xuxi Chen, Aston Zhang, Zhang…☆66Updated last year
- Official implementation for 'Sparse denoising diffusion for large graph generation'☆57Updated 10 months ago
- Implementation of the Gumbel-Sigmoid distribution in PyTorch.☆20Updated 2 years ago
- [ICLR2023] NTK-SAP: Improving neural network pruning by aligning training dynamics☆18Updated last year
- How Powerful are Spectral Graph Neural Networks☆71Updated last year
- Papers about developing DL methods on disassortative graphs☆48Updated 2 years ago
- Official implementation of GOAT model (ICML2023)☆37Updated last year
- Official implementation of our ICML 2024 paper "UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework…☆26Updated 8 months ago
- The official implementation of DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (NeurIPS 2021)☆25Updated 2 years ago
- StableGNN-Generalizing Graph Neural Networks on Out-Of-Distribution Graphs☆22Updated last year
- Official code for ICLR 2023 paper "ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond "☆34Updated last year
- Official code for the CVPR 2022 (oral) paper "OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks.…☆34Updated 3 years ago
- [ICLR 2023] 'Revisiting Pruning At Initialization Through The Lens of Ramanujan Graph" by Duc Hoang, Shiwei Liu, Radu Marculescu, Atlas W…☆12Updated last year
- The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"☆80Updated last year
- [ICLR 2023] "Equivariant Hypergraph Diffusion Neural Operators" by Peihao Wang, Shenghao Yang, Yunyu Liu, Zhangyang Wang, Pan Li☆45Updated 7 months ago
- Official implementation of Our NeurIPS 2024 Paper "Boundary Matters: A Bi-Level Active Finetuning Method"☆11Updated 2 months ago
- ☆19Updated 3 years ago
- [ICML 2023] Taxonomy-Structured Domain Adaptation☆13Updated last year
- [TPAMI 2022] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*,…☆126Updated 3 years ago