Event Detail

Event Type: 
Probability Seminar
Tuesday, February 9, 2016 - 16:00 to 17:00
WNGR 201

Speaker Info

Intel Corporation, Oregon

A graph is a representation of a collection of interacting objects. The field of pattern recognition developed significantly in the 1960s, and the field of random graph inference has enjoyed much recent progress in both theory and application. This talk focuses on pattern recognition in the context of a particular family of random graphs, namely the stochastic blockmodels, from the two main perspectives of single graph inference and joint graph inference, as well as its applications in social network, neural connectomes and digital marketing.

Single graph inference is the performance of statistical inference on one single observed graph. Given a single graph realized from a stochastic blockmodel, we here consider the specific exploitation tasks of vertex classification, clustering, and nomination. The theoretical guarantees of these methods are proved and their effectiveness are demonstrated in simulation as well as real datasets including communication network, online advertising, and neural connectomes. We are also concerned with joint graph inference, which involves the joint space of multiple graphs. Specifically, given two graphs, we consider the tasks of seeded graph matching for large graphs and joint vertex classification. The methodologies are shown to discover signals in the joint geometry of diffusion tensor MRI and the Caenorhabditis elegans neural connectomes.