简单计数法在时间图异常检测中表现优于复杂模型，且速度快数十倍。
Beat the Counter First: A Baseline for Temporal-Graph Anomaly Detectors
- 仅用单一统计特征构建基准，无需参数调优。
- 在六组数据上超越或匹配复杂模型，部分场景AUC达0.955。
- 适合评估复杂模型真实收益，尤其关注计算效率的研究者。
流式边级时间图异常检测（GAD）发展出越来越复杂的架构，从计数-最小哈希卡方检验到带记忆的注意力网络。然而，这些复杂性带来的实证增益尚未系统评估。本文提出SimpleCount，一种无参数调优的基准方法：从固定特征池（计数、最近性、首次出现指示器、计数衍生变换）中为每个数据集选择一个标量特征。我们在五个公开数据集和一个合成数据集上，将SimpleCount与两个时间图检测模型及一个基于完整特征向量拟合的IsoForest对照。结果表明，SimpleCount在六组数据中的三组上达到或超过SLADE，在全部六组上优于IsoForest。报告了配对统计检验及五次种子的SLADE评估。SLADE的墙钟时间是SimpleCount的23至133倍。在Synth-Triangle和Synth-Quad探测任务中，预事件结构分数在AUC最高达0.955，而所有评估模型仍接近随机水平。复杂性的收益取决于数据集，每项宣称的提升都应与强一特征基准及计算成本对比。
原文摘要 · Abstract (English)
Progress in streaming, edge-level graph anomaly detection (GAD) has been marked by increasingly elaborate architectures, from count-min-sketch chi square tests to memory-augmented attention networks. Yet the empirical gains attributable to this added complexity have not been systematically evaluated. We propose SimpleCount, a reference with no parameter fitting that selects one scalar feature per dataset from a fixed pool of counts, recencies, first-occurrence indicators, and count-derived transforms. We compare SimpleCount with two temporal-graph detector models and an IsoForest control fitted to the complete feature vector across five public datasets and one synthetic dataset. SimpleCount matches or exceeds SLADE on three of six datasets and exceeds IsoForest on all six. We report paired statistical tests and five-seed SLADE evaluations. SLADE requires 23 to 133x more wall-clock time than SimpleCount. On Synth-Triangle and an additional Synth-Quad probe, pre-event structural scores recover the planted signal at AUC up to 0.955, while all evaluated detector models remain near random. The benefit of complexity is dataset-dependent, and every claimed gain should be reported against a strong one-feature reference together with its compute cost.
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