The Impact of Structural Changes on Predictions of Diffusion in Networks

In a typical realistic scenario, there exist some past data about the structure of the network which are analyzed with respect to some possibly future spreading process, such as behavior, opinion, disease, or computer malware. How sensitive are the predictions made about spread and spreaders to the changes in the structure of the network? We investigate the answer to this question by considering seven real-world networks that have an explicit timeline and span a range of social interactions, from celebrity sightings to animal movement. For each dataset, we examine the results of the spread analysis with respect to the changes that occur in the network as the time unfolds as well as introduced random perturbations. We show that neither the estimates of the extent of spread for each individual nor the set of the top spreaders are robust to structural changes. Thus, analysis performed on historic data may not be relevant by the time it is acted upon.