szzhang17 / Scaling-Up-Graph-Neural-Networks-Via-Graph-CoarseningLinks
Code for the KDD 2021 paper "Scaling Up Graph Neural Networks Via Graph Coarsening"
β28Updated last year
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
Sorting:
- A scalable graph learning toolkit for extremely large graph datasets. (WWW'22, π Best Student Paper Award)β157Updated last year
- [NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodsβ124Updated 3 years ago
- β138Updated 2 years ago
- β139Updated 2 years ago
- β144Updated 2 years ago
- A paper collection about automated graph learningβ98Updated last year
- β104Updated 2 years ago
- How Powerful are Spectral Graph Neural Networksβ74Updated 2 years ago
- NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphsβ134Updated last year
- Advances on machine learning of dynamic (temporal) graphs, covering the reading list of recent top academic conferences.β63Updated 2 years ago
- A Library for Dynamic Graph Learning (NeurIPS 2023)β284Updated 2 years ago
- The official implementation of NeurIPS22 spotlight paper "NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classificaβ¦β313Updated last year
- A Critical Look at the Evaluation of GNNs under Heterophily: Are We Really Making Progress?β122Updated 2 years ago
- NeurIPS 2022, Revisiting Heterophily For Graph Neural Networks, official PyTorch implementation for Adaptive Channel Mixing (ACM) GNN fraβ¦β87Updated last year
- IJCAIβ23 Survey Track: Papers on Graph Pooling (GNN-Pooling)β117Updated 9 months ago
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)β107Updated 6 months ago
- Parameterized Explainer for Graph Neural Networkβ141Updated last year
- Code of GAMLP for Open Graph Benchmark. KDDβ22β63Updated 3 years ago
- The code of paper LMC: Fast Training of GNNs via Subgraph Sampling with Provable Convergence. Zhihao Shi, Xize Liang, Jie Wang. ICLR 2023β¦β47Updated 2 years ago
- β216Updated 2 years ago
- NeurIPS'22 Spotlight paper "Hierarchical Graph Transformer with Adaptive Node Sampling"β52Updated 2 years ago
- [IJCAI 2024] Papers about graph reduction including graph coarsening, graph condensation, graph sparsification, graph summarization, etc.β174Updated last month
- This is the GitHub repository for our ICLR22 paper: "You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks"β104Updated 2 years ago
- Dynamic Graph Benchmarkβ87Updated 2 years ago
- Largest realworld open-source graph dataset - Worked done under IBM-Illinois Discovery Accelerator Institute and Amazon Research Awards aβ¦β86Updated 6 months ago
- [NeurIPS 2025] A Python library for graph reduction including condensation, coarsening, and sparsification.β26Updated last month
- [ICLR'22][KDD'22][IJCAI'24][NeurIPS'25] Implementation of "Graph Condensation for Graph Neural Networks"β141Updated 3 months ago
- Temporal Graph Benchmark project repoβ243Updated 3 months ago
- Multilevel graph coarsening algorithm with spectral and cut guaranteesβ91Updated 5 years ago
- Learning to Drop: Robust Graph Neural Network via Topological Denoising & Robust Graph Representation Learning via Neural Sparsificationβ82Updated 4 years ago