A new benchmark suite and dataset shows that correlation clustering and ParHAC outperform the modularity-based methods commonly found in popular graph clustering libraries.
Comparison and Benchmark of Graph Clustering Algorithms
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abstract
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.
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The ParClusterers Benchmark Suite (PCBS): A Fine-Grained Analysis of Scalable Graph Clustering
A new benchmark suite and dataset shows that correlation clustering and ParHAC outperform the modularity-based methods commonly found in popular graph clustering libraries.