Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated ErdÅ's-Rényi Graphs

Osman Emre Dai, Daniel Cullina, Negar Kiyavash, Matthias Grossglauser

Research output: Contribution to journalArticle

Abstract

Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs. Recent results have characterized the exact informationtheoretic threshold for graph alignment in correlated ErdÅ's-Rényi graphs. However, very little is known about the existence of efficient algorithms to achieve graph alignment without seeds. In this work we identify a region in which a straightforward O(n11/5 logn)-Time canonical labeling algorithm, initially introduced in the context of graph isomorphism, succeeds in aligning correlated ErdÅ's-Rényi graphs. The algorithm has two steps. In the first step, all vertices are labeled by their degrees and a trivial minimum distance alignment (i.e., sorting vertices according to their degrees) matches a fixed number of highest degree vertices in the two graphs. Having identified this subset of vertices, the remaining vertices are matched using a alignment algorithm for bipartite graphs.

Original languageEnglish (US)
Pages (from-to)96-97
Number of pages2
JournalPerformance Evaluation Review
Volume47
Issue number1
DOIs
StatePublished - Dec 17 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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