A Guide to Implementing Community Detection in Apache Age
Tags: apacheage, algorithms, graphdatabase, database
Published: 2023-12-27 19:37:14

As we all know Apache Age has been a force to reckon with in the graph database space  for a while now which is offering solutions to the analytics space and also helping  data scientist to solve data issues with ease.In this article we are going to discuss/define what is community detection and how best can one implement community detection in Apache Age.

**_What is community detection ?_**
Community detection techniques are useful for social media algorithms to discover people with common interests and keep them tightly connected. Community detection can be used in machine learning to detect groups with similar properties and extract groups for various reasons. For example, this technique can be used to discover manipulative groups inside a social network or a stock market.

based on ongoing research some of this algorithms are still in the implementation stage but Apache Age supporting various programming languages such as Java,python with readily available drivers to support Apache Age one can create algorithms with the said drivers in place.

community detection algorithm that can be used in Apache Age is `Eigenvector-based community detection`.This a method used to identify communities or groups within a network by analyzing the eigenvectors of the network's adjacency matrix. The basic idea behind this approach is that nodes that belong to the same community will be more strongly connected to each other than to nodes in other communities.


This approach begins by computing the adjacency matrix of the network, capturing the relationships between nodes. Subsequently, the eigenvalues and corresponding eigenvectors of this matrix are determined. The eigenvectors associated with the highest eigenvalues are employed to categorize nodes into communities.

Essentially, the concept relies on the notion that nodes within the same community will exhibit comparable eigenvector values for these prominent eigenvectors. Through the clustering of nodes with analogous eigenvector values, the method facilitates the identification of distinct communities within the network.

The following approach can be implemented through the python programming language and below are the steps you can follow to implement this approach.


Requirements for the test:
install the apache-age community detection library
`pip install apache-age-community-detection`

Below is a sample implementation of community detection using python 

```
import time
from age_cda import Graph

# Define a new set of nodes and edges for the graph
nodes = [A, B, C, D, E]
edges = [[A, B], [A, C], [B, C], [C, D], [D, E], [D, F], [E, F]]

# Measure the execution time for the new example
start_time = time.time()

# Create a new graph and obtain the community structure
example_graph = Graph.Graph()
example_graph.createGraph(nodes,edges)
results = new_graph.get_community()

print(results)

end_time_new = time.time()
print("Elapsed time for the new example: {} seconds".format(end_time_new - start_time_new))


```
Conclusion 
This approach is one of the few that was easily found based on research but  more approaches can be researched on to achieve community detection in Apache Age.

Resource
[https://arxiv.org/pdf/physics/0605087.pdf]
[https://pypi.org/project/apache-age-community-detection/]