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Large language models (LLMs) have achieved impressive performance on manynatural language processing tasks. However, their capabilities ongraph-structured data remain relatively unexplored. In this paper, we conduct aseries of experiments benchmarking leading LLMs on diverse graph predictiontasks spanning node, edge, and graph levels. We aim to assess whether LLMs caneffectively process graph data and leverage topological structures to enhanceperformance, compared to specialized graph neural networks. Through variedprompt formatting and task/dataset selection, we analyze how well LLMs caninterpret and utilize graph structures. By comparing LLMs' performance withspecialized graph models, we offer insights into the strengths and limitationsof employing LLMs for graph analytics. Our findings provide insights into LLMs'capabilities and suggest avenues for further exploration in applying them tograph analytics.

Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data