Phantom cascades: The effect of hidden nodes on information diffusion

TL;DR

This paper investigates how hidden nodes in a network affect the accuracy of information diffusion estimates, highlighting the importance of considering hidden structures in real-world scenarios.

Contribution

It introduces a model to predict cascade size with limited network information and analyzes the impact of hidden nodes on diffusion estimation errors.

Findings

Hidden network structures cause significant underestimation of cascade sizes.

Topological properties influence the extent of estimation errors.

A practical model can predict cascade size with minimal network data.

Abstract

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.