评价链接预测方法
Evaluating Link Prediction Methods
摘要：
链接预测是一个热门的研究领域，在包括生物学、社会科学、安全和医学在内的各种学科中都有重要的应用。链接预测的基本要求是准确有效地预测网络中的新链接。虽然有许多不同的方法提出了链接预测，我们认为，这些方法的实际性能潜力往往是未知的，因为在评估链接预测，这影响了结果的可靠性和可重复性的挑战。我们描述了这些挑战，提供理论证据和实证例子，说明当前的方法如何导致可疑的结论，显示这些结论的谬误是如何照亮我们提出的方法，并制定一致的，标准的和适用的评估指标的建议。我们还建议使用精确-召回阈值曲线和相关面积来代替受试者工作特征曲线，因为链接预测分类问题中的极端不平衡会导致复杂性。
英文摘要：
Link prediction is a popular research area with important applications in a variety of disciplines, including biology, social science, security, and medicine. The fundamental requirement of link prediction is the accurate and effective prediction of new links in networks. While there are many different methods proposed for link prediction, we argue that the practical performance potential of these methods is often unknown because of challenges in the evaluation of link prediction, which impact the reliability and reproducibility of results. We describe these challenges, provide theoretical proofs and empirical examples demonstrating how current methods lead to questionable conclusions, show how the fallacy of these conclusions is illuminated by methods we propose, and develop recommendations for consistent, standard, and applicable evaluation metrics. We also recommend the use of precision-recall threshold curves and associated areas in lieu of receiver operating characteristic curves due to complications that arise from extreme imbalance in the link prediction classification problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
