Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
With the rapidly improving reasoning abilities of Large Language Models (LLMs), there is also a rising demand to use them in a wide variety of domains. This brings about the need to carefully evaluate the limits of the capabilities of these models with various tests and benchmarks. Graph structures are ubiquitous in real-world data, and are often used to represent and analyze relationship patterns within data. Many benchmarks have already been proposed in the graph literature to test the reasoning ability of LLMs to follow and execute graph algorithms. However, due to the limited context length of LLMs, these benchmarks consist of very small graphs. In real-world data, the size of graphs ...
| Authors | Sunil Kumar Maurya, Xin Liu |
|---|---|
| Primary category | cs.LG |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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