“Tensor-based methods for temporal networks”, a talk by Laetitia Gauvin at CCEGN 2019 in Les Houches (France)

Held from 6th to 10th May in the Les Houches Physics School in the Chamonix Valley of French Alps, Critical and Collective Effects in Graphs and Networks 2019 is a workshop that explores the intersections between network science, statistical physics, and random graphs. Top researchers from all the world discuss a wide range of topics, including spreading phenomena and other dynamical processes on networks, metric structure of random graphs, and statistical methods in interdisciplinary applications.

Among the invited speakers is ISI Foundation Research Leader Laetitia Gauvin, who is presenting a talk about “Tensor-based methods for temporal networks”. Starting with the description of machine learning techniques based on tensor factorization to study temporal networks (e.g. contact networks), Gauvin futherly explores techniques that detect groups of links in networks that have correlated activities. The talk focuses on the analysis of the topological structures that characterize time-varying networks, then on the interplay between the structures of time-varying networks and the dynamic processes unfolding over them, finally extending the framework of standard tensor factorization to infer missing data from a partial dataset and show how it enables us to reproduce the output of a dynamical process on the full temporal network.

Tensor-based methods for temporal networks”, a talk by Laetitia Gauvin. Critical and Collective Effects in Graphs and Networks 2019, Les Houches, 8th May 2019, 16.00. Link