Graph algorithms play a crucial role in various areas of computer science, from social network analysis to route optimization and beyond. Python, with its simplicity and versatility, provides an excellent platform for implementing and experimenting with these algorithms.
In this article, we’ll dive into Python graph algorithms, demystifying their concepts with straightforward explanations and providing practical code examples to help you grasp these essential techniques.
Understanding Graphs
Before delving into graph algorithms, it’s crucial to understand what a graph is. In simple terms, a graph is a collection of nodes (vertices) connected by edges. These connections can represent various relationships or connections between different entities. Graphs can be classified into directed and undirected graphs, depending on whether the edges have a specific direction.
class Graph:
def __init__(self):
self.graph = {}
def add_edge(self, u, v):
if u not in self.graph:
self.graph[u] = []…