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Graphs are a fundamental data structure in computer science that represent a collection of nodes (vertices) and edges connecting pairs of nodes. Graphs can model various relationships and are widely used in various applications such as social networks, transportation systems, and more.
In this tutorial, we will explore the basics of graph representation in Python, covering both directed and undirected graphs, as well as different methods for representing graphs.
There are several ways to represent a graph in Python, but two common methods are the adjacency matrix and the adjacency list.
An adjacency matrix is a 2D array where each cell matrix[i][j] represents the presence or absence of an edge between nodes i and j. For an undirected graph, the matrix is symmetric.
An adjacency list is a collection of linked lists or arrays, where each list represents the neighbors of a node.
Once the graph is represented, we may need to traverse it. Two common traversal algorithms are Depth-First Search (DFS) and Breadth-First Search (BFS).
DFS explores as far as possible along each branch before backtracking.
BFS explores all the vertices at the current depth before moving on to the vertices at the next depth.
This tutorial provides a basic understanding of graph representation and traversal in Python. Depending on the specific use case, additional algorithms and optimizations may be required.