From Symptom Networks to Conversation Networks: A Cross-Sectional Study Mapping the Topology of Suicide-Related Clinical Dialogue

Suicide risk assessment in clinical conversations relies on the sequential structure of therapist-patient dialogue, yet formal models of how suicide-related content emerges, transitions, and re-ciprocates across conversational turns are lacking. We applied network analysis to C-SSRS-coded utterances from psychotherapy sessions to characterize the topology and dynamics of suicide-related clinical communication. Transcripts of 110 psychotherapy sessions from the SPEAK-SAFE study were classified for suicidality relevance using the Qwen3-32B large language model with an adapted Columbia-Suicide Severity Rating Scale (C-SSRS) schema. From the suicidali-ty-relevant utterances, we constructed a weighted directed category transition network. Centrali-ty, reciprocity, clustering, assortativity, and path length metrics were computed. Independent human expert ratings for a session-level subsample of 30 sessions from this cohort showed sub-stantial agreement with the LLM classifications (Spearman {rho} = 0.885, ICC[2,1] = 0.708). The network was dominated by therapist inquiry. Reciprocity was high, and closeness centrality de-creased monotonically with C-SSRS severity. Active suicidal ideation functioned as a bridge node from which escalation pathways to method, plan, and attempt categories originated. The network exhibited a hub-and-spoke topology with small-world characteristics. Suicide-related clinical dialogue is structured as a therapist-centered hub-and-spoke network with high reciproci-ty and a consistent severity-peripherality gradient. The extension of network theory from intra-individual symptom structures to inter-individual communication dynamics represents a method-ological innovation demonstrated here as a proof of concept, with potential applications to clini-cal training, automated decision support, and multimodal risk assessment. clinical interviews clin-ical interviews