重新审视链接预测评估方法，提出更科学的实验设计与实践建议。
Evaluating link prediction: New perspectives and recommendations
- 构建受控实验框架，系统考察网络类型、距离分布等多因素影响。
- 发现类别不平衡显著影响早期召回率，评估指标选择需谨慎。
- 适合从事网络分析与模型评估的研究者参考最佳实践。
链接预测（LP）是网络科学与机器学习研究中的重要问题。当前主流方法通常在统一设置下评估，忽略了数据特性与应用需求的相关因素。本文识别出多个关键因素，如网络类型、问题类型、节点间测地距离及其在不同类别中的分布、方法适用性、类别不平衡及其对早期检索的影响、评估指标等，并提出一种严谨可控的实验设置。我们在多种真实网络数据集上对各类LP方法进行大规模实验，通过精心设计的假设验证这些因素如何影响性能。基于所得洞见，本文给出链接预测评估的最佳实践建议。
原文摘要 · Abstract (English)
Link prediction (LP) is an important problem in network science and machine learning research. The state-of-the-art LP methods are usually evaluated in a uniform setup, ignoring several factors associated with the data and application specific needs. We identify a number of such factors, such as, network-type, problem-type, geodesic distance between the end nodes and its distribution over the classes, nature and applicability of LP methods, class imbalance and its impact on early retrieval, evaluation metric, etc., and present an experimental setup which allows us to evaluate LP methods in a rigorous and controlled manner. We perform extensive experiments with a variety of LP methods over real network datasets in this controlled setup, and gather valuable insights on the interactions of these factors with the performance of LP through an array of carefully designed hypotheses. Following the insights, we provide recommendations to be followed as best practice for evaluating LP methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。