vthost / DAGNN
A graph neural network tailored to directed acyclic graphs that outperforms conventional GNNs by leveraging the partial order as strong inductive bias besides other suitable architectural features.
☆129Updated last year
Alternatives and similar repositories for DAGNN:
Users that are interested in DAGNN are comparing it to the libraries listed below
- [NeurIPS 2023] Implementation of "Transformers over Directed Acyclic Graphs"☆66Updated 9 months ago
- Representing Long-Range Context for Graph Neural Networks with Global Attention☆130Updated 3 years ago
- Implement of DiGCN, NeurIPS-2020☆47Updated 3 years ago
- ☆57Updated 3 years ago
- The official implementation of DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (NeurIPS 2021)☆25Updated 2 years ago
- Official Code Repository for the paper "Accurate Learning of Graph Representations with Graph Multiset Pooling" (ICLR 2021)☆106Updated 3 years ago
- PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"☆86Updated 3 years ago
- Long Range Graph Benchmark, NeurIPS 2022 Track on D&B☆158Updated last year
- A graph transformer framework☆77Updated 2 years ago
- Edge-Augmented Graph Transformer☆76Updated last year
- Rex Ying's Ph.D. Thesis, Stanford University☆42Updated 2 years ago
- NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs☆122Updated last year
- GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]☆195Updated 2 months ago
- MagNet graph convolutional network☆37Updated last year
- Parameterized Explainer for Graph Neural Network☆132Updated last year
- Official code of "Towards Multi-Grained Explainability for Graph Neural Networks" (NeurIPS 2021) + Pytorch Implementation of recent attri…☆69Updated 2 months ago
- Dir-GNN is a machine learning model that enables learning on directed graphs.☆82Updated last year
- The source code for NeurIPS 2020 paper "Graph Policy Network for Transferable Active Learning on Graphs"☆47Updated 4 years ago
- ☆156Updated 3 years ago
- How Powerful are Spectral Graph Neural Networks☆72Updated last year
- Implementation of Directional Graph Networks in PyTorch and DGL☆118Updated 4 years ago
- [ICLR 2022] Code for Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation (GLNN)☆88Updated 6 months ago
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆101Updated 3 years ago
- Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"☆94Updated 3 years ago
- "Do We Need Anisotropic Graph Neural Networks?" at ICLR 2022☆33Updated 3 years ago
- Official repository for the paper "Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting" (TPAMI'22) https://arxi…☆100Updated 3 years ago
- Graph meta learning via local subgraphs (NeurIPS 2020)☆121Updated 9 months ago
- Reimplementation of Graph Autoencoder by Kipf & Welling with DGL.☆65Updated 2 years ago
- Source code for From Stars to Subgraphs (ICLR 2022)☆70Updated last year
- [NeurIPS 2021]: Improve the GNN expressivity and scalability by decoupling the depth and receptive field of state-of-the-art GNN architec…☆133Updated 3 years ago