Neo4j Graph Data Science: Pathfinding Algorithms

Understanding how entities connect and interact across complex networks is essential for modern data analysis and routing solutions. This text-based course guides you through foundational graph theory and practical pathfinding techniques using the Neo4j Graph Data Science (GDS) framework. You will start with core terminology, graph projections, and foundational Cypher query structures before diving into algorithmic implementations. By reading detailed walkthroughs and practicing with structured query examples, you will gain the skills needed to solve real-world routing, dependency, and network traversal challenges. What you'll learn: - Understand essential graph data modeling and graph data science concepts - Configure and project in-memory graphs using the Neo4j GDS library - Apply classic Shortest Path, Dijkstra, and A* algorithms to find optimal routes - Implement Yen's algorithm and All Pairs Shortest Path for alternative and comprehensive network analysis - Utilize Random Walk and topological pathfinding methods for network exploration - Evaluate algorithm performance and memory usage for large-scale graph datasets The course progresses systematically from fundamental graph principles and syntax basics to hands-on algorithm configuration and execution. Designed specifically for beginners, no prior experience with graph algorithms is required. Start reading today to unlock actionable insights from connected graph data.