The paper introduces Structurally Speaking, a lightweight structured prompting protocol that improves graph captioning by translating explicit connectivity into concise motif-level descriptions. It treats motif-oriented graph captioning as a bidirectional graph-text translation task, aiming to preserve topology for graph recovery while producing interpretable captions. Experiments on a synthetic motif-based dataset show that the protocol yields shorter, more motif-consistent captions without sacrificing graph recovery performance.
arXiv:2511. 07457v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in modeling sequential textual data and generalizing across diverse tasks.
arXiv:2606. 11898v1 Announce Type: cross Abstract: Research on Text-Attributed Graphs (TAGs) has gained significant attention recently due to its broad applications across various real-world data scenarios, such as citation networks, e-commerce platforms, social media, and web pages.
arXiv:2506. 02568v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated substantial efficacy in advancing graph-structured data analysis.
The paper introduces PromptGFM, a Graph Foundation Model designed for text‑attributed graphs (TAGs). It integrates Large Language Models (LLMs) and Graph Neural Networks (GNNs) through a Graph Understanding Module that prompts LLMs to emulate GNN workflows, and a Graph Inference Module that creates a language‑based graph vocabulary for better alignment and scalability. Experiments show PromptGFM outperforms existing methods and transfers effectively across various graphs and tasks.
arXiv:2601. 08187v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated promising capabilities in Text-Attributed Graph (TAG) understanding.
Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains. Recent advancements in GFMs have been largely dominated by two paradigms: Graph Neural Network and Large Language Model (LLM) based methods.
arXiv:2607. 11374v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains.
arXiv:2607. 20477v1 Announce Type: new Abstract: {\em Text-Attributed Graphs} (TAGs) have emerged as an expressive data model for integrating graph topology with rich textual semantics.
arXiv:2606. 15633v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown promise for reasoning over Text-Attributed Graphs (TAGs).
arXiv:2606. 15633v2 Announce Type: replace Abstract: Large Language Models (LLMs) have shown promise for reasoning over Text-Attributed Graphs (TAGs).
The paper investigates graph soft prompting, where a graph neural network generates graph-specific tokens for large language model prompts. It identifies a problem called token collapse, in which these tokens become nearly identical across different inputs, rendering them ineffective at conveying graph information. The authors show that even simple constant prompts can match or outperform collapsed tokens, and propose a two‑stage method to produce more varied tokens while maintaining performance.
arXiv:2607. 13115v1 Announce Type: new Abstract: Small language models (SLMs) have shown promise for zero-shot molecular property prediction from SMILES strings, yet they often suffer from structural blindness because sequence representations under-specify key graph-topological cues.