Representations for temporal networks: a talk by Laetitia Gauvin at EPFL Lausanne

There has been a recent surge in the representations of networks, especially in the development of methods to create embedding for networks that preserve important properties of the original structure, while representing it in a lower dimensional space. While these kind of methods were proved to be useful for anomaly detection or prediction of links in networks, they cannot be used directly for temporal networks.

This question – also very relevant to understand what topological and structural structures are keys for dynamical processes – will be addressed by Laetitia Gauvin in “Representations for temporal networks: tensor based methods and embedding techniques”, an invited talk       on friday morning in the program of the EPFL – Engineering Workshop in Network Science in EPFL Lausanne.
     
L. Gauvin will describe an event embedding method, which represents an entire temporal network in the same reduced dimensional abstract space. To demonstrate the power of this representation, the method presented will also be used to estimate the final outcome of modelled spreading processes on several real world temporal networks.  This prediction task performs significantly better when it builds on our representation as compared to other dynamical network embeddings
       
 “Representations for  temporal networks: tensor based methods and embedding  techniques”, Laetitia Gauvin, EPFL Engineering Workshop Network Science – Foundations & Applications, EPFL Campus, Lausanne,  7th June 2019, 10.50-11.25.