Community detection is a popular graph analysis technique used to identify groups of nodes in a network that are densely connected with each other but sparsely connected to other groups. Community detection has numerous applications, such as identifying groups of people with similar interests in social networks, finding clusters of related proteins in biological networks, and identifying clusters of related websites in web link networks.

Here, we will be discussing how to install and use the age-cda Python package for community detection in the AGE graph database.

Installation

Install via PIP

You can install the age-cda package using pip, which is the easiest way to install Python packages.

pip install age-cda

Build from Source

To build age-cda from source, you need to first install libeigen3-dev. You can install it on Ubuntu/Debian based systems using the following command:

sudo apt-get update
sudo apt-get install libeigen3-dev

Next, clone the Community-Detection-Modularity repository from Github, navigate to the directory, and run the following command:

python setup.py install

You also need to configure the GNU Scientific Library (GSL). You can follow the instructions in this tutorial to configure GSL on your system.

Unit Test

You can run the unit tests using the following command:

python -m unittest test_community.py

Instruction

Import

To use age-cda in your Python project, import the Graph class from the age_cda package as shown below:

from age_cda import Graph

Create Graph

To create a graph, you need to pass two arguments to the Graph class constructor:

• Nodes: A list of nodes in the graph.
• Edges: A 2D list of edges in the graph represented as an adjacency list.

nodes = [0, 1, 2, 3, 4, 5]
edges = [[0, 1], [0, 2], [1, 2], [2, 3], [3, 4], [3, 5], [4, 5]]
g = Graph.Graph()
g.createGraph(nodes, edges)

Generate Community Assignment

To generate the community assignments, simply call the get_community() method on the graph object.

res = g.get_community()

Output Format

The get_community() method returns a list of communities, where each community is represented as a list of nodes.

[[3,4,5],[0,1,2]]

Each community is a list of nodes that belong to the community.

Conclusion

Now you can use the python AGE driver to get all nodes and edges get the desired community. In this blog, we learned how to use age-cda for community detection in Python. We covered the installation process and provided examples of how to create a graph and generate community assignments. With age-cda, you can easily analyze the structure of graphs and identify communities of nodes that are highly interconnected.