joisino / random-features
Code for "Random Features Strengthen Graph Neural Networks" (SDM 2021)
☆20Updated 3 years ago
Related projects: ⓘ
- Papers about developing DL methods on disassortative graphs☆48Updated 2 years ago
- "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')☆47Updated 2 years ago
- Pytorch implementation of differentiable group normalization (NeurIPS 2020)☆37Updated 3 years ago
- The code for the ICML 2021 paper "Graph Neural Networks Inspired by Classical Iterative Algorithms".☆43Updated 3 years ago
- Official repository for ICLR'23 paper: Multi-task Self-supervised Graph Neural Network Enable Stronger Task Generalization☆35Updated last year
- [ICLR 2023] MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization☆74Updated last year
- This repo contains a reference implementation for the paper "Breaking the Limit of Graph Neural Networks by Improving the Assortativity o…☆31Updated 2 years ago
- Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021☆45Updated 3 years ago
- [ICLR 2023] Link Prediction with Non-Contrastive Learning☆25Updated last year
- [ICML 2022] pGNN, p-Laplacian Based Graph Neural Networks☆26Updated 2 years ago
- Pytorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping"☆74Updated 2 years ago
- The implementation of our NeurIPS 2020 paper "Graph Geometry Interaction Learning" (GIL)☆44Updated 3 years ago
- Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)☆37Updated last year
- [ICLR'22] [KDD'22] [IJCAI'24] Implementation of "Graph Condensation for Graph Neural Networks"☆115Updated 2 months ago
- Implementation of "Bag of Tricks for Node Classification with Graph Neural Networks" based on DGL☆35Updated last year
- ☆35Updated last year
- [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"☆53Updated last year
- [VLDB'22] SUREL is a novel walk-based computation framework for efficient subgraph-based graph representation learning.☆18Updated last year
- A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which appeared in …☆27Updated 2 years ago
- PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)☆79Updated last year
- ☆27Updated 2 years ago
- Implementation of the paper "A New Perspective on the Effects of Spectrum in Graph Neural Networks"☆17Updated 2 years ago
- ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily☆41Updated 2 years ago
- GraphACL: Simple and Asymmetric Graph Contrastive Learning (NeurIPS 2023)☆23Updated 3 months ago
- Variational Graph Convolutional Networks☆20Updated 3 years ago
- Graph Structured Neural Network☆38Updated 2 years ago
- ☆39Updated last month
- [ICLR 2023] "Graph Domain Adaptation via Theory-Grounded Spectral Regularization" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen☆21Updated last year
- ☆43Updated 4 months ago
- Hypergraph representation learning: Hypergraph Networks with Hyperedge Neurons.☆39Updated 3 years ago