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：网络扩散模型被用于研究疾病传播、信息扩散和技术采纳等现象。然而，在构建用于实施这些模型的网络时，极有可能存在微小的测量误差。我们证明，扩散估计对这种测量误差具有高度的非稳健性。首先，我们表明，即使测量误差极小（即遗漏连边的比例接近于零），对扩散范围的预测仍会严重低估真实情况。其次，对初始种子身份的微小测量误差会导致预期扩散路径的位置发生巨大偏移。我们证明，即使测量误差仅在局部趋于零，上述两个结论依然成立。即使在基本再生数可被一致估计的条件下，这种预测的非稳健性依然存在。可能的解决方案，例如估计测量误差或实施广泛的检测措施，仍然面临困难，因为遗漏连边的数量非常少。最后，我们在模拟网络以及三种实际场景的网络数据上进行了蒙特卡洛模拟：美国西部COVID-19大流行期间的旅行数据、印度农村的手机营销活动数据以及中国的一项保险实验数据。