dfdazac / grapesLinks
Official implementation of the paper "GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks".
☆13Updated 10 months ago
Alternatives and similar repositories for grapes
Users that are interested in grapes are comparing it to the libraries listed below
Sorting:
- The codebase and datasets for the IJCAI 2021 paper "The Surprising Power of Graph Neural Networks with Random Node Initialization".☆22Updated 4 years ago
- Synthetic graph generator☆12Updated 2 years ago
- Dynamic Graph Benchmark☆88Updated 3 years ago
- Long Range Graph Benchmark, NeurIPS 2022 Track on D&B☆163Updated 2 years ago
- Temporal Graph Benchmark project repo☆246Updated 4 months ago
- ☆33Updated 3 years ago
- [WWW 2021 GLB] New Benchmarks for Learning on Non-Homophilous Graphs☆115Updated 4 years ago
- Wasserstein Weisfeiler-Lehman Graph Kernels☆86Updated last year
- Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)☆45Updated 3 years ago
- ☆65Updated last month
- Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022☆267Updated 3 years ago
- A Critical Look at the Evaluation of GNNs under Heterophily: Are We Really Making Progress?☆121Updated 2 years ago
- PyTorch Geometric Signed Directed is a signed/directed graph neural network extension library for PyTorch Geometric. The paper is accepte…☆144Updated last year
- ☆47Updated 4 years ago
- ☆85Updated 2 years ago
- [NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods☆123Updated 3 years ago
- ☆104Updated 2 years ago
- ☆155Updated 4 years ago
- code implementation of SEP(ICML 2022)☆35Updated 3 years ago
- How to Turn Your Knowledge Graph Embeddings into Generative Models☆55Updated last year
- Dir-GNN is a machine learning model that enables learning on directed graphs.☆83Updated 2 years ago
- ☆144Updated 2 years ago
- Neural Dynamics on Complex Networks☆55Updated 5 years ago
- ☆138Updated 2 years ago
- ☆86Updated 3 years ago
- Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"☆98Updated 3 years ago
- ☆19Updated 3 years ago
- PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"☆89Updated 4 years ago
- Code and dataset to test empirically the expressive power of graph pooling operators presented as presented at NeurIPS 2023☆36Updated 2 years ago
- Code for our paper "Attending to Graph Transformers"☆91Updated 2 years ago