Improved Achievability and Converse Bounds for Erdos-Renyi Graph Matching
Abstract
We consider the problem of perfectly recovering the vertex correspondence between two correlated Erdos-Renyi (ER) graphs. For a pair of correlated graphs on the same vertex set, the correspondence between the vertices can be obscured by randomly permuting the vertex labels of one of the graphs. In some cases, the structural information in the graphs allow this correspondence to be recovered. We investigate the information-theoretic threshold for exact recovery, i.e. the conditions under which the entire vertex correspondence can be correctly recovered given unbounded computational resources. Pedarsani and Grossglauser provided an achievability result of this type. Their result establishes the scaling dependence of the threshold on the number of vertices. We improve on their achievability bound. We also provide a converse bound, establishing conditions under which exact recovery is impossible. Together, these establish the scaling dependence of the threshold on the level of correlation between the two graphs. The converse and achievability bounds differ by a factor of two for sparse, significantly correlated graphs.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 14, 2016
- Source ID
- 10.1145/2964791.2901460
Entities
People
- Daniel Cullina
- Negar Kiyavash
Organizations
- National Science Foundation
- United States Army Research Laboratory
- University of Illinois Urbana–Champaign