Imagine teaching a computer to navigate a complex network, like the internet or a social network. That's essentially what researchers are trying to do when they teach Large Language Models (LLMs) to reason about graphs. Graphs, structures of nodes and edges, represent relationships between data points. They're everywhere, but LLMs, known for their text prowess, often stumble when faced with these interconnected structures. Now, a new research paper, "GraphEval2000: Benchmarking and Improving Large Language Models on Graph Datasets," introduces a powerful tool to help LLMs find their way. This research unveils GraphEval2000, a comprehensive dataset of 40 graph problems and 2000 test cases, designed to challenge and improve LLMs' graph reasoning skills. The results are revealing: LLMs are better at navigating directed graphs (where connections have a direction) than undirected ones. While private LLMs like GPT generally outperform open-source models, the gap is closing. The researchers also introduce Structured Symbolic Decomposition (SSD), a novel technique that breaks down complex graph problems into smaller, easier-to-digest steps. Think of it as giving the LLM a roadmap. SSD significantly boosted performance, especially on harder problems. This research has exciting real-world implications. By improving LLMs’ ability to reason about graphs, we can unlock their potential in areas like drug discovery (analyzing molecular structures), social network analysis, and even recommending your next Netflix binge. The challenge remains to bridge the gap between LLMs' text-based understanding and the complex world of interconnected data. GraphEval2000 provides a crucial step towards creating LLMs that can not only read and write but also truly understand the relationships that shape our world.