![]() ![]() I have opted to implement an adjacency list which stores each node in a dictionary along with a set containing their adjacent nodes.Overall you could use more descriptive names in this function. In this article, we will see how to represent a graph using an adjacency matrix in Python.There are two popular options for representing a graph, the first being an adjacency matrix (effective with dense graphs) and second an adjacency list (effective with sparse graphs). ![]() The edges in a graph can be directed or undirected. Graphs are used to represent many real-world applications such as networks, maps, and flows. A graph is a collection of nodes (vertices) and edges connecting them. Adjacency Matrix of a Directed Graph in Python. this enables the degree of a vertex to be easily found by taking the sum of the values in either its respective row or column within the adjacency matrix. Undirected Graphs: The convention followed here (for undirected graphs) is that every edge adds 1 to the acceptable cell within the matrix, and every loop adds 2. ![]() The dictionary’s keys will be the nodes, and their values will be the edges for each node. In python, we can use dictionaries to store an adjacency list. This form of representation is efficient in terms of space because we only have to store the edges for a given node. An adjacency list in python is a way for representing a graph. ![]()
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