Researchers have introduced ReDiSC, a novel reparameterized masked diffusion model designed for scalable node classification with structured predictions. This model addresses the limitation of existing graph neural networks (GNNs) that often assume conditional independence among node labels, which is not always accurate in graph structures. ReDiSC estimates the joint distribution of node labels using a diffusion model within a variational expectation-maximization framework, offering efficiency advantages and achieving competitive performance across various graph types and sizes. Notably, ReDiSC demonstrates scalability to large datasets where previous diffusion-based methods have failed. AI
IMPACT This new diffusion model offers improved scalability and accuracy for node classification tasks in graph neural networks.
RANK_REASON The cluster contains a research paper detailing a new model for node classification. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- DPM-SNC
- Gotit.pub
- graph neural networks
- Hugging Face
- ScienceCast
- Yule Liu
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