用简单图特征和传统机器学习即可高效检测随机异常
Trivial Graph Features and Classical Learning are Enough to Detect Random Anomalies
- 仅用基础图特征+经典学习方法
- 在随机注入链接上检测准确率极高
- 计算成本低，结果易解释，适合实际应用
检测表示各类交互的链路流中的异常是一个重要研究课题，具有关键应用价值。由于缺乏真实标签数据，现有方法大多通过检测随机注入的链接来评估性能。与多数依赖复杂方法（存在计算或可解释性问题）的研究不同，本文表明：仅使用简单图特征和经典学习技术，即可极佳地检测此类随机异常。该基础方法计算开销极小，结果易于理解，并在大量实验中展现出诸多优良特性。研究建议，未来检测方法应聚焦更复杂的异常类型。
原文摘要 · Abstract (English)
Detecting anomalies in link streams that represent various kinds of interactions is an important research topic with crucial applications. Because of the lack of ground truth data, proposed methods are mostly evaluated through their ability to detect randomly injected links. In contrast with most proposed methods, that rely on complex approaches raising computational and/or interpretability issues, we show here that trivial graph features and classical learning techniques are sufficient to detect such anomalies extremely well. This basic approach has very low computational costs and it leads to easily interpretable results. It also has many other desirable properties that we study through an extensive set of experiments. We conclude that detection methods should now target more complex kinds of anomalies.
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