Improved Achievability and Converse Bounds for Erdos-Renyi Graph Matching

Daniel Cullina, Negar Kiyavash

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)63-72
Number of pages10
JournalPerformance Evaluation Review
Issue number1
StatePublished - Jun 2016

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


Dive into the research topics of 'Improved Achievability and Converse Bounds for Erdos-Renyi Graph Matching'. Together they form a unique fingerprint.

Cite this