Structurally Speaking: Motif-Oriented Graph Captioning through Bidirectional Graph-Text Translation
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 enumerati...
| Authors | Hsiao-Ying Lu, Dongyu Liu, Kwan-Liu Ma |
|---|---|
| Primary category | cs.CL |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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