---------- Forwarded message ---------- From: Pierre McKenzie <mckenzie@iro.umontreal.ca> Date: 2017-02-09 17:46 GMT-05:00 Subject: Colloque DIRO, lundi 13 février, Jian Tang (Michigan) To: seminaires@iro.umontreal.ca *Learning representations of large-scale networks * par * Jian Tang * University of Michigan *Lundi 13 février, 15:30-16:30*, *Salle 6214*, Pavillon André-Aisenstadt Université de Montréal, 2920 Chemin de la Tour Café avant 15:00-15:30 *Cette présentation sera donnée en anglais.* *Résumé:* Information networks (e.g., social networks, citation networks, World Wide Web ) are ubiquitous in real world, covering a variety of applications. Traditionally, networks are usually represented as adjacency matrices. However, this type of representation is very sparse and high-dimensional, which does not facilitate computation of network analysis and network understanding. In this talk, I will introduce our recent work on learning low-dimensional representations of large-scale networks. The representations are able to facility a variety of applications such as node classification, node clustering, link prediction, recommendation, and network visualization.