Comparison and Benchmark of Graph Clustering Algorithms
This provides a comprehensive comparison for engineers and researchers in fields like biological and social network analysis, though it is incremental as it focuses on benchmarking existing methods.
The authors benchmarked over 70 graph clustering algorithms to evaluate their runtime and quality performance on weighted and unweighted graphs, analyzing how ground truth characteristics affect results.
Graph clustering is widely used in analysis of biological networks, social networks and etc. For over a decade many graph clustering algorithms have been published, however a comprehensive and consistent performance comparison is not available. In this paper we benchmarked more than 70 graph clustering programs to evaluate their runtime and quality performance for both weighted and unweighted graphs. We also analyzed the characteristics of ground truth that affects the performance. Our work is capable to not only supply a start point for engineers to select clustering algorithms but also could provide a viewpoint for researchers to design new algorithms.