Title
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. 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 we describe methods to compute or approximate convex graph invariants tractably. We discuss the interesting subclass of spectral invariants, and also compare convex and non-convex invariants. 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.
Year
DOI
Venue
2010
10.1109/CISS.2012.6310764
Information Sciences and Systems
Keywords
Field
DocType
graph theory,matrix algebra,maxcut value,adjacency matrix,approximate convex graph invariants,convex graph invariants,convex optimization,elementary invariants,k largest eigenvalues
Discrete mathematics,Combinatorics,Line graph,Cubic graph,Polyhedral graph,Null graph,Steinitz's theorem,Algebraic graph theory,Extremal graph theory,Voltage graph,Mathematics
Journal
Volume
Issue
ISSN
54
3
SIAM Review, 54(3), pp. 513-541, 2012
ISBN
Citations 
PageRank 
978-1-4673-3138-8
0
0.34
References 
Authors
11
3
Name
Order
Citations
PageRank
Venkat Chandrasekaran100.34
Pablo A. Parrilo218217.53
Alan S. Willsky37466847.01