Graph traversal is a fundamental concept in algorithms, used to explore nodes and edges in a graph. Here are common techniques and their applications:

1. Depth-First Search (DFS) 🕵️

  • Approach: Explores as far as possible along each branch before backtracking.
  • Use Cases: Maze solving, topological sorting, detecting cycles.
  • Example:
    Depth_First_Search

2. Breadth-First Search (BFS) 🧭

  • Approach: Explores all neighbors at the present depth level before moving to nodes at the next depth level.
  • Use Cases: Shortest path finding in unweighted graphs, level-order traversal.
  • Example:
    Breadth_First_Search

3. Dijkstra's Algorithm 🚀

  • Purpose: Finds the shortest path in a weighted graph with non-negative edges.
  • Key Feature: Uses a priority queue to select the next node with the smallest tentative distance.
  • Example:
    Dijkstra_Algorithm

4. A* Algorithm 🧭✨

  • Advantage: Combines Dijkstra's with heuristic estimates for efficient pathfinding.
  • Applications: Game development, route planning in maps.
  • Example:
    A_Algorithm

5. Backtracking with Graphs ⚙️

  • Scenario: Useful for solving puzzles or constraint satisfaction problems.
  • Example: Hamiltonian path detection, Sudoku solvers.

For deeper exploration, check our algorithm introduction tutorial to understand the foundation of graph theory. 📚