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.