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arXiv Machine Learning By Ucchwas Talukder Utsha, Sakib Mostafa, James Zou, Md Tauhidul Islam

Language-encoded network topology enables large language models to reason about complex networks

Read the original on arXiv Machine Learning →

The paper introduces BioGlyph, a method that translates network topology into a language of structural roles such as hubs, community cores, and cross-community connectors. By encoding these roles in a universal, interpretable vocabulary, BioGlyph allows large language models to answer structural reasoning questions about networks more accurately—improving system accuracy by up to 26 percentage points compared to edge-based or numerical representations. Experiments across twenty networks in five domains show the approach is especially effective for dense, community-structured networks and reveals biologically meaningful patterns in a budding-yeast protein-interaction network.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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