用感染节点的未感染联系人数量识别疫情场景，准确率提升19%。
Boundary Degree as a Node-level Feature for Epidemic Scenario Identification in Agent-based Cascade Simulations
- 提出边界度：感染节点未感染联系人数量作为节点级特征
- 仅用边界度使疫情场景识别准确率提升19%，优于传统聚合统计
- 适合做接触追踪系统设计，强调追踪未感染接触者的重要性
从疾病传播序列中识别疫情情景是仿真分析中的重要任务。我们提出边界度——即感染节点在底层社交网络中未被感染的联系人数量——作为该任务的节点级传播特征。在田纳西州和弗吉尼亚州的真实社交网络上进行系统消融实验表明，仅使用边界度即可将情景识别准确率提升19%。边特征（此前研究发现其重要性）在所有设置下均能持续提升准确率，本文为其提供了理论支持。两者效果互补。我们证明，若缺乏边界或边信息，某些疫情情景无法区分。以往特征工程多采用边界统计的聚合形式，但不在前列；而我们提出的节点级表示清晰揭示了其价值。结果表明，接触追踪应关注未感染接触者，而非仅追踪传播路径。
原文摘要 · Abstract (English)
Characterizing the scenario underlying an epidemic from its disease cascade is an important task in simulation analytics. We propose boundary degree, the count of an infected node's contacts in the underlying contact network that were not infected, as a per-node cascade feature for this task. Through systematic ablation on realistic social contact networks of Tennessee and Virginia, we show that boundary degree alone improves scenario identification accuracy by 19%. Edge features, whose importance was observed empirically by prior work, consistently improve accuracy across all settings; we provide theoretical grounding for this observation. These effects are complementary. We prove that certain epidemic scenarios are indistinguishable without boundary or edge information. Prior feature engineering approaches included aggregate boundary statistics, but these were not among the top-ranked feature groups; the per-node representation we propose reveals their importance clearly. Our results suggest that contact tracing applications should track contacts with non-infected individuals, not only transmissions.
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