Performance Evaluation of Python Libraries for Community Detection on Large Social Network Graphs

Abstract

The development of social networking platforms has transformed the way people interact. The increasing number of social media users generates vast amounts of data and creates communities among users that are interesting to analyze. Community detection holds significant benefits in understanding network structures and providing insights into these communities. Community detection can be performed using various libraries and algorithms. However, the abundance of options can be challenging in selecting the appropriate library and algorithm. Therefore, this research evaluates the performance of community detection algorithms on social network graph datasets using several Python libraries such as NetworkX, igraph, Scikit-Network, and CDlib, as well as Louvain and Label Propagation algorithms. The findings of this study reveal that igraph is an ideal library, with fast execution time, efficient memory usage, and user-friendliness. Additionally, the Louvain algorithm is ideal for community detection and exhibits high modularity values.