Vision-Language Models (VLMs) have emerged as versatile solutions for zero-shot question answering (QA) across various domains. However, enabling VLMs to effectively comprehend structured graphs and perform accurate, efficient QA remains challenging. Existing approaches typically rely on one single graph topology representation (GTR), such as fixed-style visual images or unified text descriptions. This ``one-size-fits-all'' strategy often neglects model-specific and task-specific preferences, resulting in inaccurate or over-lengthy responses to graph-related queries. To address this, we propose the $\mbox{DynamicGTR}$ framework, which dynamically selects the optimal GTR for each query during inference, thereby enhancing the zero-shot graph QA capabilities of VLMs with a customizable accuracy and brevity trade-off. Extensive experiments show that DynamicGTR not only improves VLM-based graph algorithm QA performance but also successfully transfers the experience trained from synthetic graph algorithm tasks to real-world applications like link prediction and node classification, without any additional training. Additionally, DynamicGTR demonstrates strong transferability across tasks, domains, and models, suggesting its potential as a flexible solution for broad graph scenarios.

翻译：视觉语言模型（VLMs）已成为跨领域零样本问答（QA）的通用解决方案。然而，如何使VLMs有效理解结构化图并执行准确、高效的问答仍然具有挑战性。现有方法通常依赖单一的图拓扑表示（GTR），例如固定风格的视觉图像或统一的文本描述。这种“一刀切”的策略往往忽略了模型特定和任务特定的偏好，导致对图相关查询的响应不准确或过于冗长。为解决这一问题，我们提出了$\mbox{DynamicGTR}$框架，该框架在推理过程中为每个查询动态选择最优的GTR，从而以可定制的准确性与简洁性权衡来增强VLMs的零样本图问答能力。大量实验表明，DynamicGTR不仅提升了基于VLM的图算法问答性能，而且成功地将从合成图算法任务中训练获得的经验迁移到链接预测和节点分类等现实应用中，无需任何额外训练。此外，DynamicGTR在任务、领域和模型间展现出强大的可迁移性，表明其有潜力成为广泛图场景的灵活解决方案。