szzhang17 / Scaling-Up-Graph-Neural-Networks-Via-Graph-Coarsening
Code for the KDD 2021 paper "Scaling Up Graph Neural Networks Via Graph Coarsening"
☆27Updated 9 months ago
Alternatives and similar repositories for Scaling-Up-Graph-Neural-Networks-Via-Graph-Coarsening:
Users that are interested in Scaling-Up-Graph-Neural-Networks-Via-Graph-Coarsening are comparing it to the libraries listed below
- MagNet graph convolutional network☆37Updated last year
- Parameterized Explainer for Graph Neural Network☆130Updated last year
- NeurIPS'22 Spotlight paper "Hierarchical Graph Transformer with Adaptive Node Sampling"☆50Updated last year
- A pytorch implementation of graph transformer for node classification☆30Updated last year
- ☆57Updated 3 years ago
- ☆131Updated last year
- [NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods☆122Updated 2 years ago
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆101Updated 2 years ago
- ☆77Updated 2 years ago
- [ICML 2022] Local Augmentation for Graph Neural Networks☆66Updated 9 months ago
- ☆99Updated last year
- Learning to Drop: Robust Graph Neural Network via Topological Denoising & Robust Graph Representation Learning via Neural Sparsification☆77Updated 3 years ago
- NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs☆121Updated last year
- Dynamic Graph Benchmark☆77Updated 2 years ago
- How Powerful are Spectral Graph Neural Networks☆71Updated last year
- ☆52Updated 3 years ago
- This is the GitHub repository for our ICLR22 paper: "You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks"☆94Updated last year
- Implementation of "Bag of Tricks for Node Classification with Graph Neural Networks" based on DGL☆35Updated 2 months ago
- An Empirical Evaluation of Temporal Graph Benchmark☆32Updated last year
- Adversarial Graph Augmentation to Improve Graph Contrastive Learning☆88Updated 3 years ago
- PyTorch implementation of "BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation"☆54Updated last year
- Code for paper https://arxiv.org/abs/2102.13186☆44Updated 3 years ago
- Advances on machine learning of dynamic (temporal) graphs, covering the reading list of recent top academic conferences.☆57Updated last year
- Lifelong Learning of Graph Neural Networks for Open-World Node Classification☆29Updated last year
- ☆135Updated last year
- A collection of graph data used for semi-supervised node classification.☆39Updated 2 years ago
- ☆58Updated 4 years ago
- A Critical Look at the Evaluation of GNNs under Heterophily: Are We Really Making Progress?☆112Updated 2 years ago
- A collection of papers on Graph Structural Learning (GSL)☆54Updated last year
- ☆24Updated 2 years ago