Graph data is ubiquitous in the physical world, and it has always been a
challenge to efficiently model graph structures using a unified paradigm for
the understanding and reasoning on various graphs. Moreover, in the era of
large language models, integrating complex graph information into text
sequences has become exceptionally difficult, which hinders the ability to
interact with graph data through natural language instructions.The paper
presents a new paradigm for understanding and reasoning about graph data by
integrating image encoding and multimodal technologies. This approach enables
the comprehension of graph data through an instruction-response format,
utilizing GPT-4V's advanced capabilities. The study evaluates this paradigm on
various graph types, highlighting the model's strengths and weaknesses,
particularly in Chinese OCR performance and complex reasoning tasks. The
findings suggest new direction for enhancing graph data processing and natural
language interaction.

通过整合图像编码和多模态技术，将图数据理解和推理的范式与 GPT-4V 的先进能力相结合，以指令 - 响应格式实现对图数据的理解，评估该范式在不同图类型上的性能并强调模型的优势和劣势，特别是中文 OCR 性能和复杂推理任务，为增强图数据处理和自然语言交互提供了新方向。