A review and comparative analysis of coarsening algorithms on bipartite networks

Abstract

Coarsening algorithms have been successfully used as a powerful strategy to deal with data-intensive machine learning problems defined in bipartite networks, such as clustering, dimensionality reduction, and visualization. Their main goal is to build informative simplifications of the original network at different levels of details. Despite its widespread relevance, a comparative analysis of these algorithms and performance evaluation is needed. Additionally, some aspects of these algorithms’ current versions have not been explored in their original or complementary studies. In that regard, we strive to fill this gap, presenting a formal and illustrative description of coarsening algorithms developed for bipartite networks. Afterward, we illustrate the usage of these algorithms in a set of emblematic problems. Finally, we evaluate and quantify their accuracy using quality and runtime measures in a set of thousands of synthetic and real-world networks with various properties and structures. The presented empirical analysis provides evidence to assess the strengths and shortcomings of such algorithms. Our study is a unified and useful resource that provides guidelines to researchers interested in learning about and applying these algorithms.