Project 4: STL-Based Graph Library

A simple, header-only graph library that can represent directed or undirected graphs. The key is that the entire implementation will use STL containers instead of manual memory management, representing an adjacency list as a map of nodes to vectors of neighbors. You’ll then implement BFS and DFS traversals using STL container adaptors.

Quick Reference

Attribute Value
Primary Language C++
Alternative Languages Python, Java
Difficulty Level 3: Advanced
Time Estimate 1-2 weeks
Knowledge Area Data Structures / Graph Theory / API Design
Tooling N/A
Prerequisites Solid C++ skills, understanding of basic graph algorithms.

What You Will Build

A simple, header-only graph library that can represent directed or undirected graphs. The key is that the entire implementation will use STL containers instead of manual memory management, representing an adjacency list as a map of nodes to vectors of neighbors. You’ll then implement BFS and DFS traversals using STL container adaptors.

Why It Matters

This project builds core skills that appear repeatedly in real-world systems and tooling.

Core Challenges

  • Designing the graph representation → maps to choosing the right nested containers, e.g., std::unordered_map<T, std::vector<T>>
  • Implementing graph modification methods → maps to add_edge, add_node, etc., by manipulating the underlying containers
  • Implementing Depth-First Search (DFS) → maps to using a std::stack to manage the nodes to visit
  • Implementing Breadth-First Search (BFS) → maps to using a std::queue to manage the nodes to visit

Key Concepts

  • Container Adaptors (stack, queue): Wrappers around other containers to provide a specific interface. (cppreference.com)
  • Modeling with the STL: Representing abstract data structures with standard containers.
  • Templates: Making your graph library work with any node type (int, string, etc.).

Real-World Outcome

// Your library in action
#include "graph.h"
int main() {
    Graph<std::string> g;
    g.add_edge("A", "B");
    g.add_edge("B", "C");
    g.add_edge("A", "D");

    std::cout << "DFS from A: ";
    g.dfs("A", [](const std::string& node) {
        std::cout << node << " ";
    }); // Output: A D B C (or similar)

    std::cout << "\nBFS from A: ";
    g.bfs("A", [](const std::string& node) {
        std::cout << node << " ";
    }); // Output: A B D C (or similar)
}

Implementation Guide

  1. Reproduce the simplest happy-path scenario.
  2. Build the smallest working version of the core feature.
  3. Add input validation and error handling.
  4. Add instrumentation/logging to confirm behavior.
  5. Refactor into clean modules with tests.

Milestones

  • Milestone 1: Minimal working program that runs end-to-end.
  • Milestone 2: Correct outputs for typical inputs.
  • Milestone 3: Robust handling of edge cases.
  • Milestone 4: Clean structure and documented usage.

Validation Checklist

  • Output matches the real-world outcome example
  • Handles invalid inputs safely
  • Provides clear errors and exit codes
  • Repeatable results across runs

References

  • Main guide: LEARN_CPP_STL_DEEP_DIVE.md
  • “Data Structures and Algorithm Analysis in C++” by Mark Allen Weiss