Springer’s Social Network Analysis and Mining (SNAM), a prestigious journal, publishes groundbreaking research at the intersection of computational and information sciences discipline and the social science discipline. Recently, SNAM published one of our studies titled, “A Comparative Evaluation of Social Network Analysis Tools: Performance and Community Engagement Perspectives.” The authors present a comparative evaluation of five popular social network analysis tools: NetworkX, RustworkX, Igraph, EasyGraph, and Graph-tool. It benchmarks their performance across various network analysis methods, including community detection, centrality measures, path algorithms, and calculations for modularity, density, and path length using different datasets.

Performance analysis reveals that Igraph and Graph-tool consistently emerged as the most efficient tools, with Igraph excelling specifically in modularity, density, and path length calculations. EasyGraph demonstrated particular strengths in community detection and finding connected components.

Interestingly, while NetworkX is the most popular tool based on community engagement metrics (downloads, stars, forks), it exhibits slower performance in most benchmarks compared to the alternatives. The authors suggest NetworkX’s continued popularity may be due to its well-documented methods and user-friendly API rather than computational performance.

The research shows that performance varies based on several factors including graph structure, implementation language (with compiled languages like C++ outperforming interpreted ones like Python), and algorithmic optimizations. 

Prof. Agarwal said, “This comprehensive benchmarking study illuminates the performance tradeoffs among leading network analysis frameworks. These findings help practitioners make informed decisions when selecting social network analysis tools, balancing specific performance requirements with user-friendliness.”

Click here to read the full article.