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.

粗化算法已成功用作处理二部网络中定义的数据密集型机器学习问题的强大策略，例如聚类、降维和可视化。他们的主要目标是在不同的细节层次上对原始网络进行信息简化。尽管具有广泛的相关性，但仍需要对这些算法和性能评估进行比较分析。此外，这些算法当前版本的某些方面尚未在其原始或补充研究中进行探索。在这方面，我们努力填补这一空白，对为二部网络开发的粗化算法进行正式和说明性的描述。之后，我们将说明这些算法在一组标志性问题中的用法。最后，我们在具有各种属性和结构的数千个合成和现实世界网络中使用质量和运行时间度量来评估和量化它们的准确性。所呈现的经验分析为评估此类算法的优缺点提供了证据。我们的研究是一个统一且有用的资源，可为有兴趣学习和应用这些算法的研究人员提供指导。