Graph algorithms are powerful analytics tools that help explore and understand complex networks, revealing patterns and structures within data. They provide structured ways to navigate and analyze relationships between entities in various domains such as social networks, maps, organizational charts, or system components. Graph algorithms can uncover insights that traditional methods often miss, leading to improved decisions and better outcomes. By analyzing the connections within data, graph algorithms reveal hidden patterns and structures that can be used to answer questions like "How are these two entities connected?", "What's the shortest/cheapest/fastest route between A and B?", or "Which nodes are the most influential or critical?". These algorithms come in various types, including pathfinding, centrality, community detection, similarity, link prediction, and node embedding techniques. Graph algorithms are used across industries to tackle real-world problems such as detecting sophisticated fraud, powering smarter recommendation engines, optimizing supply chains and logistics, simplifying identity and access management, and enhancing machine learning with graph features. Neo4j Aura Graph Analytics provides an extensive library of 65+ pre-built graph algorithms that can be easily accessed and applied to data, making it a powerful tool for uncovering connected insights and improving decision-making.