The graph is a fundamental data structure in computer science that represents a collection of nodes connected by edges. It is widely used to model relationships between entities, such as social networks, computer networks, and transportation systems. In Python, there are several ways to implement a graph data structure, each with its own advantages and use cases.

## Adjacency Matrix

One common way to represent a graph is by using an adjacency matrix. This is a two-dimensional array where each element represents whether there is an edge between two nodes.

The value in the matrix can be either 0 or 1, indicating the absence or presence of an edge respectively. For example:

`<table>`

<tr>

<td>**0**</td>

<td>**1**</td>

<td>**1**</td>

<td>**0**</td>

</tr>

<tr>

<td>**1**</td>

<td colspan="3">**0**</td>

)
__
____
</ul&r>__
__
____
__### Adjacency List

An alternative way to represent a graph is by using an adjacency list. This involves storing each node's neighbors in a list or dictionary. Each node points to a list of its adjacent nodes.

`<ul>`

<li>**0**: [1, 2]</li>

<li>**1**: []</li>

<li>**2**: [0]</li>

<li>**3**: []</li>

</ul&r>
### NetworkX Library

In Python, the NetworkX library provides a comprehensive set of tools for working with graphs. It offers implementations for various graph types, algorithms for graph analysis, and visualization capabilities.

`<p><b><u><i>Note:</i></u> To use NetworkX, you need to install it first using the command <i><b>pip install networkx</b></i>. Then you can import it into your Python code using <i><b>import networkx as nx</b></i>.`

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