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

When Graph Data Meets Multimodal: A New Paradigm for Graph Understanding and Reasoning