Inadequacy of SIR Model to Reproduce Key Properties of Real-world Spreading Phenomena: Experiments on a Large-scale P2P System


Daniel Bernardes, Matthieu Latapy, Fabien Tarissan

In Journal of Social Network Analysis and Mining, 3(4):1195-1208,Springer, 2013.


Understanding the spread of information on complex networks is a key issue from a theoretical and applied
perspective. Despite the effort in developing theoretical models
for this phenomenon, gauging them with large-scale real-world
data remains an important challenge due to the scarcity of
open, extensive and detailed data. In this paper, we explain
how traces of peer-to-peer file sharing may be used to this goal.
We reconstruct the underlying social network of peers sharing
content and perform simulations on it to assess the relevance of
the standard SIR model to mimic key properties of real spreading
cascades. First we examine the impact of the network topology
on observed properties. Then we turn to the evaluation of two
heterogeneous extensions of the SIR model. Finally we improve
the social network reconstruction, introducing an affinity index
between peers, and simulate a SIR model which integrates this
new feature. We conclude that the simple, homogeneous model
is insufficient to mimic real spreading cascades. Moreover, none
of the natural extensions of the model we considered, which
take into account extra topological properties, yielded satisfying
results in our context. This raises an alert against the careless,
widespread use of this model.

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