Large Language Models (LLMs) are impressive, but they sometimes hallucinate facts and struggle with complex reasoning. Imagine trying to answer a question like, "Who directed the highest-grossing film starring the lead actor from 'The Matrix'?" An LLM might get some details right but fumble the connections. That's where knowledge graphs come in. They act like a structured information web, linking entities and their relationships. New research explores how to effectively use these graphs to help LLMs reason more accurately. Researchers have developed a system called SubgraphRAG, which retrieves relevant sub-sections of a knowledge graph related to a specific question. Instead of overwhelming the LLM with the entire graph, SubgraphRAG provides bite-sized, relevant chunks of information. Think of it like giving the LLM a cheat sheet with just the facts it needs. This approach helps smaller LLMs, like the 8-billion parameter Llama model, achieve impressive accuracy. Even more powerful LLMs, like GPT-4, achieve state-of-the-art results when given these helpful subgraphs. This targeted approach not only improves accuracy but also helps explain *why* the LLM arrived at a particular answer, making the whole reasoning process more transparent. One key innovation is the use of directional structural distances. These distances tell the system how closely related different entities are within the knowledge graph, allowing it to prioritize the most relevant information. By feeding LLMs information that's both relevant and concise, this research paves the way for more reliable, explainable, and efficient AI reasoning.