Graph Algorithms: Finding All Paths Between Two Nodes

Navigating complex networks and finding routes is a fundamental challenge in software development, from routing engines to social network analysis. Understanding how to systematically discover every possible path between two points is a cornerstone of graph theory and algorithmic problem-solving. In this text-based course, you will build a solid foundation in graph representations and traversal strategies. You will progress from basic terminology to writing clean, optimized algorithms that find all paths between nodes, while analyzing their performance and handling edge cases. What you'll learn: - Understand core graph concepts, including directed, undirected, cyclic, and acyclic structures. - Represent graphs in code using modern data structures and type hints. - Implement depth-first search (DFS) with backtracking to systematically discover paths. - Manage memory and prevent infinite loops in graphs with cycles. - Analyze the time and space complexity of pathfinding algorithms. - Practice writing clean, readable code with structured text exercises. The course begins with essential terminology and graph representations before guiding you through the step-by-step logic of pathfinding algorithms. You will work through structured written explanations, code walkthroughs, and practical exercises designed to solidify your understanding of graph traversal. This course is designed for beginner programmers, computer science students, and self-taught developers who want to master fundamental graph algorithms. No prior experience with graph theory is required, though basic familiarity with programming concepts like loops and recursion is helpful. Start learning today and unlock the power of graph-based problem solving.