Peter Orbanz

Wednesday - 7 November 2018

Time: 4.00pm

Ground Floor Seminar Room

25 Howland Street, London, W1T 4JG

Statistical models of large graphs and networks

Relational data is, roughly speaking, any form of data that can be represented as a graph: A social network, user preference data, protein-protein interactions, etc. A recent body of work, by myself and others, aims to develop a statistical theory of such data for problems where a single graph is observed (such as a small part of a large social network). Keywords include graphon, edge-exchangeable and sparse exchangeable graphs, and many latent variable models used in machine learning. I will summarize the main ideas and results of this theory: How and why the exchangeability assumptions implicit in commonly used models for such data may fail; what can be done about it; what we know about convergence; and implications of these results for methods popular in machine learning, such as graph embeddings and empirical risk minimization.