**Zoubin Ghahramani**

http://mlg.eng.cam.ac.uk/zoubin/

Wednesday 20th February 2013

**Time: 4pm**

** **

Basement Seminar Room

Alexandra House, 17 Queen Square, London, WC1N 3AR

**Bayesian nonparametric modelling of networks**

Network data and more generally relational data encoding the pairwise relations between objects appear in many fields. For instance in biology, a protein network connects interacting partners, while in a social network, links among people indicate social relationships. The problems of analysing, understanding and modelling such networks have attracted interest from many research communities. I will briefly review some probabilistic approaches to modelling networks. The key idea behind many such models is that each object has certain latent features, and that observed links in the network depend on these latent features. Probabilistic inference allows one to discover the potentially unbounded number of latent features (including discovering communities as a special case), predict missing links, and generally learn about the statistical properties of the networks. Many of these models can be cast within the theoretical framework of exchangeable arrays established by Aldous, Hoover and Kallenberg. I will describe our work on a general network model (the Random Function Model) that instantiates this theory using Gaussian processes, and relate it to existing models. I will also discuss our work on the Infinite Latent Attribute (ILA) model which allows for a highly structured nonparametric latent variable representation of nodes in a network. Finally, I will describe our Latent Feature Propagation model for dynamic networks. What ties these models together is the idea that rich latent representations underlie the structure of networks, and that these can be discovered via Bayesian inference.

Joint work with Creighton Heaukulani, David A. Knowles, James Lloyd, Peter Orbanz, Konstantina Palla, and Dan Roy.