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Research on information diffusion generally assumes complete knowledge of theunderlying network. However, in the presence of factors such as increasingprivacy awareness, restrictions on application programming interfaces (APIs)and sampling strategies, this assumption rarely holds in the real world whichin turn leads to an underestimation of the size of information cascades. Inthis work we study the effect of hidden network structure on informationdiffusion processes. We characterise information cascades through activationpaths traversing visible and hidden parts of the network. We quantify diffusionestimation error while varying the amount of hidden structure in five empiricaland synthetic network datasets and demonstrate the effect of topologicalproperties on this error. Finally, we suggest practical recommendations forpractitioners and propose a model to predict the cascade size with minimalinformation regarding the underlying network.

Phantom cascades: The effect of hidden nodes on information diffusion