Spreading Rumours without the Network
Abstract
In this paper we tackle the following question: is it possible
to predict the characteristics of the evolution of an epidemic
process in a social network on the basis of the degree distribution
alone? We answer this question armatively
for
several di↵usion processes– Push-Pull, Broadcast and SIR–
by showing that it is possible to predict with good accuracy
their average evolution. We do this by developing a space ef-
ficient predictor that makes it possible to handle very large
networks with very limited computational resources. Our
experiments show that the prediction is surprisingly good
for many instances of real-world networks. The class of real-world
networks for which this happens can be characterized
in terms of their neighbourhood function, which turns out to
be similar to that of random networks. Finally, we analyse
real instances of rumour spreading in Twitter and observe
that our model describes qualitatively well their evolution.