A comparative evaluation of social network analysis tools: performance and community engagement perspectives

Abstract Graphs are increasingly used in research, industry, and government. This has led to a wide range of analytical and graph-processing tools. There are various tools and platforms for graph processing. Over time, diverse systems have emerged, employing various statistics and criteria to evaluate their effectiveness and usability in processing graph data. As a result, comparing the various systems’ performances becomes challenging. This study benchmarks popular network analysis tools—NetworkX, RustworkX, Igraph, EasyGraph, and Graph-tool—by evaluating their performance and extracted community engagement metrics, such as the number of downloads, stars, and forks which reflect the tools’ adoption, popularity, and community support. We benchmark the library’s performance on four open-source datasets, one custom dataset, and twelve network analysis methods. The findings reveal that while NetworkX is highly popular, it exhibits slower performance in most benchmarks compared to Graph-tool and Igraph, which are faster and more efficient despite their lower popularity. The continued popularity of NetworkX may be attributed to factors like well-documented methods and a user-friendly API, though this warrants further investigation. This research provides valuable insights for practitioners, researchers, and developers, helping them make informed decisions when selecting network analysis tools. The study emphasizes the trade-off between user-friendliness and performance, suggesting that the optimal tool choice depends on project-specific requirements.