Structurally Speaking: Motif-Oriented Graph Captioning through Bidirectional Graph-Text Translation

2026-09-09

AI Summary

本文通过双向图文转换方法，解决了图标题生成中结构信息保留与简洁性之间的矛盾，提出了一种轻量级结构化提示协议以生成更紧凑且一致的图标题。

Abstract

Graph captions should help readers understand graph structure, rather than simply translate adjacency matrices into long textual edge lists. A useful graph caption abstracts connectivity into recognizable motifs, such as hubs, paths, cycles, cliques, and bridges, because these motifs provide compact structural units that are easier to read, compare, and recover. In this paper, we study motif-oriented graph captioning as a bidirectional graph-text translation task, where captions must both preserve enough topology for graph recovery and express the graph through concise motif-level descriptions. We show that direct prompting of GPT-5.1 often produces graph-recoverable captions by enumerating node-to-node connections, but these captions are verbose and can contain inconsistent motif interpretations. To address this gap, we introduce Structurally Speaking, a lightweight structured prompting protocol that guides translation between explicit connectivity and motif-level abstraction. Experiments on a synthetic motif-based dataset show that structured prompting produces shorter and more motif-consistent captions while maintaining comparable graph recovery. These results suggest that explicit topology-to-motif reasoning guidance can make LLM-generated graph captions more interpretable without model fine-tuning.

Problem

• graph captioning
• motif-oriented
• topology
• graph recovery
• structured prompting

Innovation

• bidirectional graph-text translation
• motif-oriented graph captioning
• structured prompting protocol