Coping with incompleteness: a paper about estimating the outcome of spreading processes on networks built from partial information

Recent advances in data collection have facilitated the access to human proximity data that can conveniently be represented as temporal networks of contacts between individuals. While the structural and dynamical information revealed by this type of data is fundamental to investigate how information or diseases propagate in a population, data often suffer from incompleteness, which possibly leads to biased estimations in data-driven models. A major challenge is to estimate the outcome of spreading processes occurring on temporal networks built from partial information.

How to cope with it? A team of scientists including ISI Foundation Scientific Director Ciro Cattuto, ISI researchers Laetitia Gauvin and Alain Barrat, and former ISI researcher Anna Sapienza (USC Information Sciences Institute) devised an approach based on non-negative tensor factorization, a dimensionality reduction technique from multilinear algebra. The key idea is to learn a low-dimensional representation of the temporal network built from partial information and to use it to construct a surrogate network similar to the complete original network.

Researchers considered several human-proximity networks, on which they performed resampling experiments to simulate a loss of data. Using this approach on the resulting partial networks, they built a surrogate version of the complete network for each. Results are highlighted on a new paper out in Physical Review E.
Estimating the outcome of spreading processes on networks with incomplete information: A dimensionality reduction approach, Anna Sapienza,Alain Barrat, Ciro Cattuto, and Laetitia Gauvin,Physical Review E, 30 July 2018. Link: