A Python NetworkX weighted graph is a way to store and analyze networks and graphs. It is the basic concept of a graph by assigning numerical values (weights) to the edges or nodes of the graph.

NetworkX library is a popular Python library for working with graphs. It provides a flexible framework for creating, analyzing, and visualizing weighted graphs.

Each node has a weight in a weighted graph that tells us how far it is. These weights can model various real-world scenarios and problems. Here's what you need to know about weighted graphs and their applications:

- Edge Weighted and Directed Graphs
- Node Weighted Graphs

NetworkX is a Python library for working with graph objects. It allows you to create and analyze various types of networkx packages. NetworkX can create various types of complex networks.

- Undirected Graphs
- Directed Graphs (NetworkX DiGraph)
- Multigraphs
- Directed Multigraphs (MultiDiGraphs)
- Weighted Graphs
- Bipartite Graphs

Creating and using weighted graphs in NetworkX to analyze data involves several steps. Here's a step-by-step guide on how to create and use NetworkX weighted graphs to analyze data:

- Import NetworkX Library
- Prepare Your Data
- Create an Empty Graph
- Add Nodes and Edges
- Access and Modify Weights
- Analyze the Weighted Graph
- Visualization

**The use of NetworkX weighted graphs:**

- It offers a powerful toolset for understanding complex processes.
- It can capture nuanced information and make data-driven decisions.

NetworkX can analyze data by using weighted graphs. This analysis can help improve supply chains or identify important players. It contributes to better-informed business strategies and operations.

Fig: Preview of the output that you will get on running this code from your IDE

### Code

In this solution we are using networkx library

### Instructions

__Follow the steps carefully to get the output easily.__

- Download and Install the PyCharm Community Edition on your computer.
- Open the terminal and install the required libraries with the following commands.
- Create a new Python file on your IDE.
- Copy the snippet using the 'copy' button and paste it into your python file.
- Add the word plt.show() in last line of the code for better understanding
- Run the current file to generate the output.
**

I hope you found this useful.

*I found this code snippet by searching for ' **Networkx Graph plot node weights**' in Kandi. You can try any such use case!*

### Environment tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

- PyCharm Community Edition 2023.1
- The solution is created in Python 3.11.1 Version
- Networkx 0.7.2 version

Using this solution, we can able to create weighted graph in networkx with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to create weighted graph in networkx.

### Dependency library

matplotlibby matplotlib

matplotlib: plotting with Python

matplotlibby matplotlib

Python 17559 Version:v3.7.1 License: No License

*You can search for any dependent library on kandi like ' **networkx**' and ' **matplotlib**'.*

**FAQ:**

**1. What is a simple NetworkX graph, and what does it consist of? **

You can create a basic graph using NetworkX in Python. It's called a simple NetworkX graph. It consists of the following components:

- Nodes
- Edges
- Graph Structure

**2. How can I create a random weighted graph in Python? **

You can use the NetworkX library and Python's random module to make a random weighted graph in Python. Here's how you can create a random weighted graph:

- We create an empty graph using nx.Graph().
- We specify the number of nodes (num_nodes) and the number of edges (num_edges) you want in the graph.
- We add nodes to the graph using G.add_nodes_from().
- We use a loop to add random weighted edges to the graph. In the loop, we randomly pick two nodes to make an edge and assign a random weight using random.uniform().
- Finally, we print the edges and their associated weights.

**3. What are the key attributes of a graph object? **

NetworkX graphs have attributes that give information about their properties and structure. It can also have metadata. The key attributes of a graph object include:

- Nodes
- Edges
- Graph Type
- Graph Size
- Node and Edge Attributes

**4. How can I use networks to analyze data? **

Here's a step-by-step guide on how to use networks to analyze data:

- Data Representation
- Data Collection and Preparation
- Graph Construction
- Centrality Analysis and Descriptive Analysis
- Community Detection and Path Analysis
- Prediction and Recommendation

**5. How do I create an empty graph with NetworkX in Python? **

In NetworkX, you can create an empty graph in Python using the nx.Graph() constructor. Here's how to create an empty graph:

*import networkx as nx *

* # Create an empty undirected graph *

*G = nx.Graph() *

In the code above:

- import networkx as nx imports the NetworkX library with the alias nx for convenience.
- nx.Graph() creates an empty undirected graph and assigns it to the variable G.

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