LLMs Meet GNNs for Authorship Attribution: Metric Geometry and OT Calibration

Authorship attribution has long relied on function words, which capture syntactic style independently of topic and anonymize the text. Word adjacency networks (WANs) model each text as a Markov chain over function words, and distances between them define a syntactic geometry over a corpus. We exploit this geometry with graph neural networks (GNNs), which process large language model (LLM) embeddings of the anonymized texts as signals over a graph built from WAN distances. GNNs are stable and transferable across geometric graphs, but these guarantees hold only if the graph itself is well-behaved. We therefore design the graph so that its distances form a valid metric, remain stable under small edits, and keep distinct styles separated, and so that its edge weights make informative edges stand out. We characterize how candidate metrics respond to small perturbations, trace a token-level edit through every stage of the pipeline to prove stability and discriminability, and introduce an optional calibration step that reshapes the edge weights through a monotone, bi-Lipschitz optimal transport map. On Early Modern English plays, the calibrated GNN reaches 73.2% accuracy, outperforming an LLM classifier at 70.0% and an uncalibrated GNN at 70.5%, and calibration reduces the accuracy drop under WAN perturbations from 7.3% to 0.3%.