---------- 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 *

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* 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.