Network diffusion models are used to study things like disease transmission, information spread, and technology adoption. However, small amounts of mismeasurement are extremely likely in the networks constructed to operationalize these models. We show that estimates of diffusions are highly non-robust to this measurement error. First, we show that even when measurement error is vanishingly small, such that the share of missed links is close to zero, forecasts about the extent of diffusion will greatly underestimate the truth. Second, a small mismeasurement in the identity of the initial seed generates a large shift in the locations of expected diffusion path. We show that both of these results still hold when the vanishing measurement error is only local in nature. Such non-robustness in forecasting exists even under conditions where the basic reproductive number is consistently estimable. Possible solutions, such as estimating the measurement error or implementing widespread detection efforts, still face difficulties because the number of missed links are so small. Finally, we conduct Monte Carlo simulations on simulated networks, and real networks from three settings: travel data from the COVID-19 pandemic in the western US, a mobile phone marketing campaign in rural India, and in an insurance experiment in China.

翻译：网络扩散模型用于研究疾病传播、信息扩散和技术采纳等现象。然而，在构建这些模型所依托的网络时，极有可能存在少量测量误差。研究表明，扩散估计对这些测量误差高度非鲁棒。首先，即使测量误差小到几乎可以忽略（即缺失连接的比例接近于零），关于扩散范围的预测仍将严重低估真实情况。其次，初始种子身份识别上的微小误差也会导致预期扩散路径位置出现大幅偏移。我们证明，即使这种可忽略的测量误差仅具有局部性质，上述结论依然成立。在基本再生数可稳定估计的条件下，这类预测非鲁棒性依然存在。可能的解决方案，如估计测量误差或实施大规模检测措施，仍面临困难，因为缺失连接的数量实在太小。最后，我们在模拟网络及三类真实网络上进行了蒙特卡洛模拟：美国西部COVID-19疫情期间的旅行数据、印度农村的手机营销活动数据，以及中国的保险实验数据。