kilianhae / discrete_DPPM_GraphsLinks
Diffusion Models for Graphs Benefit From Discrete State Spaces
☆34Updated 2 years ago
Alternatives and similar repositories for discrete_DPPM_Graphs
Users that are interested in discrete_DPPM_Graphs are comparing it to the libraries listed below
Sorting:
- EDGE: Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling☆57Updated 3 weeks ago
- Official implementation for 'Sparse denoising diffusion for large graph generation'☆61Updated last year
- Implementation for the paper: GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation☆28Updated 2 years ago
- Reference implementation for SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators (ICML …☆26Updated 2 years ago
- Permutation-Invariant Autoregressive Diffusion (NeurIPS 2024)☆15Updated 9 months ago
- [ICLR 2023] "Equivariant Hypergraph Diffusion Neural Operators" by Peihao Wang, Shenghao Yang, Yunyu Liu, Zhangyang Wang, Pan Li☆45Updated 10 months ago
- NeurIPS2022-Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure☆40Updated last year
- [KDD 2022] Implementation of "Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective"☆45Updated last year
- Ratioanle-aware Graph Contrastive Learning codebase☆43Updated 2 years ago
- Official implementation for GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs (NeurIPS 20…☆20Updated 2 years ago
- [AAAI2023 Oral] The official implementation of "Hierarchical Contrastive Learning for Temporal Point Processes"☆25Updated 2 months ago
- A list of papers for group meeting☆16Updated last month
- An implementation of the Autoregressive Diffusion Model for Graph Generation from [Kong et al. 2023]☆36Updated 5 months ago
- A collection of papers and resources about Data Centric Graph Machine Learning (DC-GML)☆40Updated last year
- This is the official code repository for "Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs", wh…☆88Updated last year
- [ECCV'22] Equivariant Hypergraph Neural Networks, in PyTorch☆30Updated 2 years ago
- [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"☆59Updated 2 years ago
- Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models☆25Updated last year
- Official Code Repository for the paper "Graph Generation with Diffusion Mixture" (ICML 2024).☆33Updated last year
- ☆12Updated last year
- Source code for From Stars to Subgraphs (ICLR 2022)☆69Updated last year
- PyTorch implementation of GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks☆36Updated 2 years ago
- [ICLR 2023] MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization☆77Updated 2 years ago
- A Quasi-Wasserstein Loss for Learning Graph Neural Networks (QW loss)☆10Updated last year
- [ICLR 2023] Learnable Randomness Injection (LRI) for interpretable Geometric Deep Learning.☆23Updated last year
- Pytorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping"☆80Updated 3 years ago
- [ICLR 2024] VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs☆99Updated last year
- Official Implementation of "D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion"☆21Updated last year
- Official implementation for the paper: Permutation Invariant Graph Generation via Score-Based Generative Modeling☆110Updated last year
- "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')☆48Updated 3 years ago