Uncovering, understanding, and predicting links


Linked data are ubiquitous. Friends are connected to friends in social networks, genes interact with genes in biological networks, and papers cite other papers in citation networks. Uncovering, understanding, and making predictions using such links is of critical importance in unlocking and applying this data.

In this talk I will present probabilistic models for these tasks. Our approaches leverage the machinery of topic models, such as latent Dirichlet allocation (LDA), which have been successfully employed for a variety of applications. However, topic models typically make naive assumptions about the independence of documents. By breaking these naive assumptions, we can develop novel models that give insights into the latent structure of networks of documents. By using efficient variational inference, I will show that these models can make accurate predictions and extract new link information from free text.

Venue, Date, and Time

Venue: Newell Simon Hall 1507

Date: Monday, March 30, 2009

Time: 12:00 noon