发表机构
Technical University of Denmark (DTU)（丹麦技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究证明时间网络中节点自激发与连边强化在发起者可观测时正交，但无向数据中边际化导致结构性混杂；经验数据中节点记忆近乎不可识别，而连边强化可识别，且低估疫情规模至多2.5倍。
AI 中文摘要
时间网络模型将接触数据中的记忆归因于节点自激发（分支比 n_node）或连边强化（κ），这对流行病传播具有重大影响。我们证明，当事件发起者可观测时，这两种机制是正交的：Fisher 信息矩阵呈块对角结构，二者互不权衡。在无向邻近数据中，由于发起者不可观测，对其边际化处理将两种机制耦合为结构性混杂，且该混杂在后验平滑后依然存在。然而，在经验邻近、消息和电子邮件记录中，一种更粗糙的失效模式占主导：拟合的节点记忆被钉在事件间隔边际分布上，并在潜在标签后验样本间几乎保持不变（变异系数低于 1%）。事件间隔顺序洗牌检验和突发性-记忆诊断表明，指数-霍克斯节点记忆是从无中恢复的，而连边强化始终可识别。这种近乎不可识别性是内在的，而非指数核的伪影：在合成幂律自激发过程上重新拟合灵活的尺度无关（指数和）核，无法区分真实节点记忆与无记忆更新控制，且在算法网络（编辑机器人、云微服务）和皮层尖峰数据上出现相同的崩溃。下游流行病后果是定量的：拟合经验接触记录的模拟将疫情规模低估至多 2.5 倍，并移动了流行阈值。我们得出结论，观测性时间网络面临双重可识别性边界：接触方向性对于解耦连边强化至关重要，而重尾节点自激发仅从接触时间上本质上不可识别。
英文摘要
Temporal-network models attribute memory in contact data to either node self-excitation (branching ratio n_node) or tie reinforcement (kappa), carrying major consequences for epidemic spreading. We prove that when event initiators are observed, the two mechanisms are orthogonal: the Fisher information is block-diagonal and neither trades off against the other. In undirected proximity data, where initiators are unobserved, marginalising over them couples the mechanisms into a structural confound that survives posterior smoothing. On empirical proximity, messaging, and email records, however, a cruder failure dominates: fitted node memory is pinned to the inter-event marginal law and remains virtually invariant across latent label posterior samples (coefficient of variation below 1%). An inter-event-order shuffle test and burstiness-memory diagnostics reveal that exponential-Hawkes node memory is recovered from none, while tie reinforcement remains identifiable throughout. This near-unidentifiability is intrinsic, not an artefact of the exponential kernel: refitting flexible scale-free (sum-of-exponentials) kernels on synthetic power-law self-exciting processes fails to distinguish genuine node memory from memoryless renewal controls, with identical collapses recurring on algorithmic networks (edit bots, cloud microservices) and cortical spiking. Downstream epidemic consequences are quantitative: simulations fitted to empirical contact records under-predict outbreak sizes by up to a factor of 2.5 and shift the epidemic threshold. We conclude that observational temporal networks face a two-fold identifiability boundary: contact directionality is essential to decouple tie reinforcement, whereas heavy-tailed node self-excitation is intrinsically unidentifiable from contact timings alone.
Comments25 pages, 7 figures; 33 pages Supplementary Material
