arXiv Machine Learning June 19, 2026 By Itsuki Nakagawa, Kenji Yamanishi

An Information Theoretic Framework for Graph Novelty Generation via Latent Mixture Modeling

Read the original on arXiv Machine Learning →

arXiv:2606. 19770v1 Announce Type: new Abstract: We propose an information-theoretic framework for graph novelty generation, which aims to generate data that are distinct from existing patterns while preserving global structural consistency.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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