Get Started
Home
Topics
Search
Library
Research questionHow can LLMs reason about network structure when graphs are presented as edge lists, prose, or tables?Questions about centrality, community bridges, and the effects of removing nodes depend on relationships that are difficult to infer from linear text or measurement tables. This makes network topology challenging for language models to use in structural reasoning.
AI
Machine Learning
Natural Language Processing
Reasoning
Research Paper
Technology
Latest papersRecent research connected to this question, newest first.Language-encoded network topology enables large language models to reason about complex networksThe source studies open LLMs answering network-structure questions using representations derived from topology, across 20 networks spanning five domains. Evidence includes a budding-yeast protein-interaction network and shows stronger gains for dense, community-structured networks than for sparse networks; it does not establish performance for every network type or language model.research paper · Sep 3, 2026
Related questions
How can graph learning leverage visual graph depictions for structural reasoning?How can we tell whether LLM hidden-state geometry reflects reasoning operations rather than lexical or positional cues?How can we test whether language models genuinely execute multi-step graph logic when static benchmarks become contaminated?How can LLMs answer knowledge-intensive questions over large, unseen knowledge graphs without graph-specific tuning?