幻影级联：隐藏节点对信息扩散的影响
Phantom cascades: The effect of hidden nodes on information diffusion
摘要：
对信息扩散的研究通常假设对底层网络的完全了解。然而，在诸如隐私意识的提高、应用程序编程接口（API）和采样策略的限制等因素的存在下，这种假设在现实世界中很少成立，这反过来又导致低估了信息级联的大小。在这项工作中，我们研究了隐藏的网络结构对信息扩散过程的影响。我们通过激活路径遍历网络的可见和隐藏部分来传递信息。我们量化扩散估计误差，而不同的隐藏结构的数量在五个经验和合成的网络数据集，并展示了拓扑属性对这个错误的影响。最后，我们提出了实用的建议，为从业者，并提出了一个模型来预测级联的大小与底层网络的最小信息。
英文摘要：
Research on information diffusion generally assumes complete knowledge of the underlying network. However, in the presence of factors such as increasing privacy awareness, restrictions on application programming interfaces (APIs) and sampling strategies, this assumption rarely holds in the real world which in turn leads to an underestimation of the size of information cascades. In this work we study the effect of hidden network structure on information diffusion processes. We characterise information cascades through activation paths traversing visible and hidden parts of the network. We quantify diffusion estimation error while varying the amount of hidden structure in five empirical and synthetic network datasets and demonstrate the effect of topological properties on this error. Finally, we suggest practical recommendations for practitioners and propose a model to predict the cascade size with minimal information regarding the underlying network.
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