  Our research integrates graph data with Large Language Models (LLMs), which,
despite their advancements in various fields using large text corpora, face
limitations in encoding entire graphs due to context size constraints. This
paper introduces a new approach to encoding a graph with diverse modalities,
such as text, image, and motif, coupled with prompts to approximate a graph's
global connectivity, thereby enhancing LLMs' efficiency in processing complex
graph structures. The study also presents GraphTMI, a novel benchmark for
evaluating LLMs in graph structure analysis, focusing on homophily, motif
presence, and graph difficulty. Key findings indicate that the image modality,
especially with vision-language models like GPT-4V, is superior to text in
balancing token limits and preserving essential information and outperforms
prior graph neural net (GNN) encoders. Furthermore, the research assesses how
various factors affect the performance of each encoding modality and outlines
the existing challenges and potential future developments for LLMs in graph
understanding and reasoning tasks. All data will be publicly available upon
acceptance.
