Boundary Degree as a Node-level Feature for Epidemic Scenario Identification in Agent-based Cascade Simulations
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 c...
| Authors | Amro Alabsi Aljundi, Galen Harrison, Jiangzhuo Chen et al. |
|---|---|
| Primary category | cs.SI |
| Published | 28 June 2026 |
| Last updated at source | 6 July 2026 |
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