LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?

Understand

In an era marked by the increasing adoption of Large Language Models (LLMs) for various tasks, there is a growing focus on exploring LLMs' capabilities in handling web data, particularly graph data.

• Dynamic graphs, which capture temporal network evolution patterns, are ubiquitous in real-world web data.
• Evaluating LLMs' competence in understanding spatial-temporal information on dynamic graphs is essential for their adoption in web applications, which remains unexplored in the literature.
• In this paper, we bridge the gap via proposing to evaluate LLMs' spatial-temporal understanding abilities on dynamic graphs, to the best of our knowledge, for the first time.