Convex Graph Invariants

Abstract:

The structural properties of graphs are usually characterized in terms of invariants, which are functions of graphs that do not depend on the labeling of the nodes. We discuss convex graph invariants, which are graph invariants that are convex functions of the adjacency matrix of a graph. Some examples include functions of a graph such as the maximum degree, the MAXCUT value (and its semidefinite relaxation), and spectral invariants such as the sum of the k largest eigenvalues. Such functions can be used to construct convex sets that impose various structural constraints on graphs, and thus provide a unified framework for solving a number of interesting graph problems via convex optimization. We give a representation of all convex graph invariants in terms of certain elementary invariants, and describe methods to compute or approximate convex graph invariants tractably. We also discuss connections to robust optimization. Finally we use convex graph invariants to provide efficient convex programming solutions to graph problems such as the deconvolution of the composition of two graphs into the individual components, hypothesis testing between graph families, and the generation of graphs with certain desired structural properties.

Joint work with Pablo Parrilo and Alan Willsky.

Biography:

Venkat Chandrasekaran received the B.S. degree in electrical engineering and the B.A. degree in mathematics from Rice University in 2005. He received the S.M. degree in 2007 in electrical engineering from the Massachusetts Institute of Technology, where he is currently a Ph.D. candidate in the Laboratory for Information and Decision Systems. His research interests include optimization, statistics, and signal processing.