What makes the graph data structure very interesting and powerful is the large number of algorithms that can be run on graphs to extract insights. Common graph algorithms include traversing a graph and computing the shortest path between nodes. Implementing these algorithms is a great way to learn how graphs are explored and optimized. In this course, learn how graphs can be traversed by studying both depth-first and breadth-first graph traversal and discover how they can be implemented using a stack and a queue respectively. Next, explore how to compute the shortest path in an unweighted graph. And finally, use Dijkstra's algorithm to compute the shortest path in a weighted graph. Upon completion of this course, you will be able to implement optimal algorithms on graphs.

In order to really understand how graphs work, it is important to know how they are implemented. There are multiple ways to represent graphs in code and each representation has its own advantages and disadvantages. In this course, you will implement graphs using three different representations - the adjacency matrix, the adjacency list, and the adjacency set. Learn how the adjacency matrix representation uses a square matrix to represent connections between the nodes of a graph and also edge weights. Next, explore how the adjacency list suffers from a major drawback: the same graph can have multiple representations. Finally, discover how the adjacency set representation has exactly one way in which a graph is represented. When you are finished with this course, you will be able to create and work with your own graph structures and optimize them for different purposes.

Graphs are used to model a large number of real-world scenarios, including professional networks, flight networks, and schedules. Working in these problem domains involves a deep understanding of how graphs are represented and how graph algorithms work. Learn the basic components of a graph and how nodes and edges can be used to model relationships. Examine how domains such as social networks, purchases on an e-commerce platform, and connected devices can be modeled using graphs. Next, explore how to use an organizing principle to add semantic meaning and context to graphs. Discover how to apply higher-level organizing principles to knowledge graphs using taxonomies and ontologies. Finally, get hands-on experience creating and manipulating graphs, and running graph algorithms using the NetworkX library in Python. When you have completed this course, you will have a solid understanding of how graphs model entities and relationships in the real world.