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#graph-discovery News & Analysis

2 articles tagged with #graph-discovery. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles
AINeutralarXiv – CS AI · Jun 116/10
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From Uniform to Learned Graph Priors: Diffusion for Structure Discovery

Researchers propose Diff-prior, a diffusion-based adaptive prior system that improves neural relational inference (NRI) methods for discovering interaction graphs from data. Rather than relying on oversimplified uniform priors that treat edges independently, the new approach uses learned denoising-style calibration to produce more reliable and decisive structural discoveries across multiple NRI architectures.

AIBullisharXiv – CS AI · Apr 65/10
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Efficient Causal Graph Discovery Using Large Language Models

Researchers propose a new framework using Large Language Models for causal graph discovery that requires only linear queries instead of quadratic, making it more efficient for larger datasets. The method uses breadth-first search and can incorporate observational data, achieving state-of-the-art results on real-world causal graphs.