This module outline covers the core concepts of graph data structures — including definitions of vertices and edges, directed vs. undirected graphs, adjacency matrix and adjacency list representations, and graph traversal algorithms — serving as a navigational guide for learners and instructors to see the full scope of topics in the module.
Module Artifacts · 10 items
A foundational reading that introduces graph data structures by explaining what graphs are, why they are more flexible than arrays, linked lists, and trees, and where they appear in real-world computing; learners read it to build conceptual context before studying specific graph topics.
A reading that distinguishes directed graphs (edges with a one-way direction) from undirected graphs (symmetric edges), covering related terminology such as degree, paths, and cycles, so learners can correctly classify and reason about graph relationships.
A reading that explains the adjacency matrix storage format for graphs, covering how to build and interpret the matrix, its memory cost, and the time complexity of common operations, so learners can evaluate when this representation is appropriate.
A reading that explains the adjacency list storage format for graphs, covering how lists of neighbors are stored, their memory and time-complexity trade-offs compared with adjacency matrices, and how to implement them, so learners can choose and apply the most practical representation for sparse graphs.
A reading that explains the Breadth-First Search algorithm, covering its layer-by-layer traversal logic, queue-based implementation, visited tracking, and use cases such as shortest-path finding in unweighted graphs, so learners understand how to explore graphs level by level.
A reading that explains the Depth-First Search algorithm, covering its recursive and stack-based implementations, backtracking behavior, visited tracking, and applications such as cycle detection and topological sorting, so learners understand how to explore graphs along deep paths before backtracking.
A hands-on coding reading that walks learners through building a JavaScript Graph class from scratch, then adding BFS and DFS methods step by step, so they can translate traversal concepts into working, runnable code.
A hands-on coding reading that walks learners through building a JavaScript Graph class from scratch, then adding BFS and DFS methods step by step, so they can translate traversal concepts into working, runnable code.
This Veritasium video explores the mathematical and algorithmic principles behind how Google Maps computes routes so efficiently. It examines the graph-based representation of road networks and the optimization strategies that make real-time pathfinding feasible at massive scale. The video connects foundational graph theory to a widely recognized, high-impact real-world application.