Explore the basics of graph theory, learn to represent graphs in C++, and master essential algorithms like DFS and Dijkstra to solve complex optimization problems, including matching and network flow.

Graph algorithms are the core of many real-world applications of computer science,  such as automotive navigation or routing in computer networks. They’re also a common subject in coding interviews at top-tier tech companies.
 
In this course, we’ll learn about the basic concepts of graph theory and how to represent graphs as data structures in code. We’ll study essential graph algorithms such as depth-first search or Dijkstra's algorithm to traverse graphs and find shortest paths. Finally, we’ll learn to solve more complex optimization problems on graphs, such as matching and network flow problems.

Learn Graph Algorithms in C++

## Introducing weighted graphs

**Weighted graphs** are a slight generalization of graphs that are useful for modeling more complex problems using graph theory. In an (edge)-weighted graph, each edge is assigned a **weight**, which is usually a number that represents a property of the edge.

For example, consider this undirected graph of subway stations, where each edge represents a train connection. The weight of an edge is a real number representing the distance between the two nodes it connects.

# Introducing weighted graphs

**Weighted graphs** are a slight generalization of graphs that are useful for modeling more complex problems using graph theory. In an (edge)-weighted graph, each edge is assigned a **weight**, which is usually a number that represents a property of the edge.

For example, consider this undirected graph of subway stations, where each edge represents a train connection. The weight of an edge is a real number representing the distance between the two nodes it connects.

Introduction

Graph Representations

Graph Traversal

Shortest Paths

Spanning Trees

Flow Problems

Conclusion

Appendix

Weighted Graphs

Introducing weighted graphs