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This research presents "Structurally Speaking," a prompting protocol that enhances Large Language Models' ability to generate concise, motif-based descriptions of graph structures rather than verbose edge lists. By guiding LLMs to abstract connectivity into recognizable units like hubs and cycles, the method improves caption interpretability and recovery accuracy without requiring model fine-tuning. This advancement addresses a key limitation in current graph representation learning by bridging explicit topology with high-level structural reasoning.
Structurally Speaking: Motif-Oriented Graph Captioning through Bidirectional Graph-Text Translation
arXiv:2609.10923v1 Announce Type: cross 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.
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.
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2609.10923 [cs.CL]
(or arXiv:2609.10923v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.10923 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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