Convex Graph Invariants

TL;DR

This paper introduces convex graph invariants, a class of functions that are convex with respect to the adjacency matrix, enabling the use of convex optimization to solve various graph problems efficiently.

Contribution

It characterizes all convex graph invariants, provides methods for their computation, and demonstrates their application in solving complex graph problems through convex programming.

Findings

Convex graph invariants include spectral and degree-based functions.

They enable convex optimization approaches for graph deconvolution and hypothesis testing.

The framework unifies various graph problems under convex optimization techniques.

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. In this paper we study 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 compare convex and non-convex invariants, and 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.