MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation (NeurIPS 2026)
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Updated
Oct 15, 2026 - Python
MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation (NeurIPS 2026)
Information-Geometric Adaptive Sampling for Graph Diffusion
Code for "Scaling Diffusion Models to Large Sparse Graphs"
atlas — few-label learning on a manifold. One label per class recovers ~95% of MNIST when the metric is good enough (graph diffusion), and hurts when it isn't. Visual, reproducible study of edge purity, phase transitions, and label budgets.
Enhancing Spatiotemporal Supply Chain Forecasting Using Virtual Node-Augmented Graph Diffusion for Improved Fuel Efficiency
Latent Graph Diffusion 论文复现 | 图扩散生成 + 节点分类 | PyTorch + PyG
Official implementation of CoPHo (KDD 2026).
Awesome Graph Diffusion Models is a collection of graph generation works, including papers, codes and datasets.
[ICLR 2023] "Equivariant Hypergraph Diffusion Neural Operators" by Peihao Wang, Shenghao Yang, Yunyu Liu, Zhangyang Wang, Pan Li
[ICML 2023] Official implementation of "A randomized schur complement based graph augmentor"
[NeurIPS GLFrontiers 2023] DIGNN, Implicit Graph Neural Diffusion Models
Supervised graph diffusion and fusion.
SM-NetFusion for supervised multi-topology network cross-diffusion.
Brain Graph Super-Resolution: how to generate high-resolution graphs from low-resolution graphs?
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