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、信息扩散和技术采用等现象。然而，在构建这些模型所需网络时，极有可能存在少量测量误差。我们证明扩散估计对这种测量误差高度非鲁棒。首先，即便测量误差极小（如遗漏链接的比例趋近于零），关于扩散范围的预测也会严重低估真实情况。其次，初始种子身份上的微小测量误差会导致预期扩散路径位置发生巨大偏移。我们证明，即使这种趋于零的测量误差仅具有局部性质，上述结论依然成立。这种预测非鲁棒性甚至存在于基本再生数可被一致估计的条件下。可能的解决方案，如估计测量误差或实施广泛的检测策略，仍面临困难，因为遗漏链接数量极少。最后，我们在模拟网络及三类真实网络（美国西部的COVID-19旅行数据、印度农村手机营销活动、中国保险实验）中进行了蒙特卡洛模拟。