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How to create a complete graph in Networkx

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by Abdul Rawoof A R dot icon Updated: Sep 27, 2023

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A NetworkX complete graph is a type of graph in the NetworkX. This Python library represents a basic, undirected graph. Only one edge connects each pair of different nodes. 


In other words, in a complete graph, there is an edge between every pair of nodes, and there are no self-loops. We denote a complete graph with n nodes as K_n.   


Here are some common types of networks that you can create using NetworkX:   

Social Networks:   

  • Friendship Networks: Model friendships or relationships between individuals.   
  • Professional Networks: Represent professional connections between people.   
  • Collaboration Networks: Analyze collaborations between authors, researchers, or artists.   

Transportation Networks:   

  • Road Networks: Model road systems for transportation and routing.   
  • Public Transportation Networks: Create networks for subways, buses, or trains.   
  • Flight Networks: Analyze air travel connections between airports.   

Infrastructure Networks:   

  • Power Grids: Represent the electrical power distribution system.   
  • Water Distribution Networks: Model water supply and distribution.   
  • Telecommunication Networks: Analyze data or phone communication networks.   

Biological Networks:   

  • Protein Interaction Networks: Study interactions between proteins in a biological system.   
  • Gene Regulatory Networks: Model gene interactions and regulatory pathways.   
  • Food Webs: Represent predator-prey relationships in ecosystems.   

  

Understanding NetworkX, from its basic features to its more advanced features. Working with complex network data is important for several reasons. Here are some key reasons for its importance:   

  • Fundamental Understanding of Graph Theory   
  • Data Representation   
  • Graph Visualization   
  • Basic Algorithms   
  • Advanced Algorithms   

  

Here is an example of how to create a complete graph in Networkx:   

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

Code

In this solution, we are using the Networkx, NumPy, and Netgraph libraries.

Create graph with curved and labelled edges in Networkx

PythondotLines of Code : 33dotLicense : Strong Copyleft (CC BY-SA 4.0)

import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

from netgraph import Graph # pip install netgraph

tab = ("r", ["s", "t", "u", "v", "w", "x", "y", "z"])

producer = tab[0]
consumers = tab[1]

DG = nx.DiGraph()
for i, cons in enumerate(consumers):
    DG.add_edge(producer, cons, label=f"edge-{i}")

node_color = dict()
for node in DG:
    if node in producer:
        node_color[node] = "#DCE46F"
    else:
        node_color[node] = "#6FA2E4"

pos = nx.shell_layout(DG)
pos[producer] = pos[producer] + np.array([0.2, 0])
edge_labels = nx.get_edge_attributes(DG, 'label')

Graph(DG, node_layout=pos, edge_layout='curved', origin=(-1, -1), scale=(2, 2),
      node_color=node_color, node_size=8.,
      node_labels=True, node_label_fontdict=dict(size=10),
      edge_labels=edge_labels, edge_label_fontdict=dict(size=10),
)
plt.show()

Instructions

Follow the steps carefully to get the output easily.

  1. Install PyCharm Community Edition on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Install Networkx - pip install networkx.
  4. Install Netgraph - pip install netgraph.
  5. Install NumPy - pip install numpy.
  6. Create a new Python file(e.g.: test.py).
  7. Copy the snippet using the 'copy' button and paste it into that file.
  8. Run the file using the run button.


I hope you found this useful. I have added the link to dependent libraries, and version information in the following sections.


I found this code snippet by searching for 'Create graph with curved and labelled edges in Networkx' 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.

  1. The solution is created in PyCharm 2022.3.3.
  2. The solution is tested on Python 3.9.7.
  3. Networkx version 3.1.
  4. NumPy version 1.24.2.
  5. Netgraph version current.


Using this solution, we are able to create a complete graph in Networkx with simple steps. This process also facilitates an easy-to-use, hassle-free method to create a hands-on working version of code which would help us to create a complete graph in Networkx.

Dependent Libraries

networkxby networkx

Python doticonstar image 12745 doticonVersion:networkx-3.1doticon
License: Others (Non-SPDX)

Network Analysis in Python

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            networkxby networkx

            Python doticon star image 12745 doticonVersion:networkx-3.1doticon License: Others (Non-SPDX)

            Network Analysis in Python
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                      numpyby numpy

                      Python doticonstar image 23755 doticonVersion:v1.25.0rc1doticon
                      License: Permissive (BSD-3-Clause)

                      The fundamental package for scientific computing with Python.

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                                numpyby numpy

                                Python doticon star image 23755 doticonVersion:v1.25.0rc1doticon License: Permissive (BSD-3-Clause)

                                The fundamental package for scientific computing with Python.
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                                          netgraphby paulbrodersen

                                          Python doticonstar image 506 doticonVersion:Currentdoticon
                                          License: Strong Copyleft (GPL-3.0)

                                          Publication-quality network visualisations in python

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                                                    netgraphby paulbrodersen

                                                    Python doticon star image 506 doticonVersion:Currentdoticon License: Strong Copyleft (GPL-3.0)

                                                    Publication-quality network visualisations in python
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                                                              You can also search for any dependent libraries on kandi like 'Networkx', 'Netgraph' and 'NumPy'.

                                                              FAQ:   

                                                              1. What is a cycle graph, and what are its applications?   

                                                              A cycle graph is often referred to as a cyclic graph. It is a type of graph in graph theory that consists of a single closed loop. In simpler terms, it's a graph that has nodes and edges. You can move from one node to another by following the edges. 

                                                                 

                                                              2. How does a toroidal 2-dimensional grid graph differ from other networkx complete graphs?   

                                                              A toroidal 2D grid graph is different from other complete graphs in NetworkX. 

                                                              • Topology   
                                                              • Connectivity   
                                                              • Edge Density   
                                                              • Use Cases   

                                                                 

                                                              3. Can we use ladder graphs to model social networks?   

                                                              Ladder graphs are not used to model social networks. Ladder graphs have nodes arranged in two parallel rows. There are edges connecting nodes from one row to the other in a ladder-like fashion. These graphs are often used in mathematics and computer science for theoretical purposes.   


                                                              4. What properties do all graph vertices in a networkx complete graph have in common?   

                                                              All the vertices in a complete graph, also called nodes, have many things in common. 

                                                              • Connectivity   
                                                              • Degree   
                                                              • Edge Count   
                                                              • Symmetry   
                                                              • Density   
                                                              • Unique Paths   

                                                                 

                                                              5. Can algorithms predict links for complete graphs in networkx?   

                                                              Link prediction algorithms are well-designed to predict missing edges in networks. But, predicting missing edges in a complete graph is quite simple. All the possible edges are already present. An edge connects every pair of nodes in a complete graph, so there are no missing edges to predict.  

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