现有链接预测评估方法有缺陷，新采样方案更可靠。
Reevaluation of Inductive Link Prediction
- 用更大负样本集重评模型性能，避免小样本偏差
- 简单类型规则基线在原方法下表现超前
- 适合评估链接预测模型的公平性与可复现性
本文指出当前归纳式链接预测的评估协议存在严重缺陷，因其依赖于在少量随机采样的负样本中对真实实体进行排序。由于负样本集过小，一个基于实体类型的简单规则基线即可达到最先进的性能。基于此发现，我们采用通常用于转导设置的链接预测协议，在多个基准上重新评估了现有归纳方法。部分归纳方法在此新设置下因可扩展性问题表现不佳，因此我们提出了改进的采样协议，避免前述问题。实验结果与以往报告结果显著不同。
原文摘要 · Abstract (English)
Within this paper, we show that the evaluation protocol currently used for inductive link prediction is heavily flawed as it relies on ranking the true entity in a small set of randomly sampled negative entities. Due to the limited size of the set of negatives, a simple rule-based baseline can achieve state-of-the-art results, which simply ranks entities higher based on the validity of their type. As a consequence of these insights, we reevaluate current approaches for inductive link prediction on several benchmarks using the link prediction protocol usually applied to the transductive setting. As some inductive methods suffer from scalability issues when evaluated in this setting, we propose and apply additionally an improved sampling protocol, which does not suffer from the problem mentioned above. The results of our evaluation differ drastically from the results reported in so far.
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