  Large language models (LLMs) have achieved impressive performance on many
natural language processing tasks. However, their capabilities on
graph-structured data remain relatively unexplored. In this paper, we conduct a
series of experiments benchmarking leading LLMs on diverse graph prediction
tasks spanning node, edge, and graph levels. We aim to assess whether LLMs can
effectively process graph data and leverage topological structures to enhance
performance, compared to specialized graph neural networks. Through varied
prompt formatting and task/dataset selection, we analyze how well LLMs can
interpret and utilize graph structures. By comparing LLMs' performance with
specialized graph models, we offer insights into the strengths and limitations
of employing LLMs for graph analytics. Our findings provide insights into LLMs'
capabilities and suggest avenues for further exploration in applying them to
graph analytics.
