Community Recovery in a Preferential Attachment Graph

Bruce Hajek, Suryanarayana Sankagiri

Research output: Contribution to journalArticle

Abstract

A message passing algorithm is derived for recovering communities within a graph generated by a variation of the Barabási-Albert preferential attachment model. The estimator is assumed to know the arrival times, or order of attachment, of the vertices. The derivation of the algorithm is based on belief propagation under an independence assumption. Two precursors to the message passing algorithm are analyzed: the first is a degree thresholding (DT) algorithm and the second is an algorithm based on the arrival times of the children (C) of a given vertex, where the children of a given vertex are the vertices that attached to it. Comparison of the performance of the algorithms shows it is beneficial to know the arrival times, not just the number, of the children. The probability of correct classification of a vertex is asymptotically determined by the fraction of vertices arriving before it. Two extensions of Algorithm C are given: the first is based on joint likelihood of the children of a fixed set of vertices; it can sometimes be used to seed the message passing algorithm. The second is the message passing algorithm. Simulation results are given.

Original languageEnglish (US)
Article number8771226
Pages (from-to)6853-6874
Number of pages22
JournalIEEE Transactions on Information Theory
Volume65
Issue number11
DOIs
StatePublished - Nov 2019

Keywords

  • Preferential attachment graph
  • clustering
  • community recovery
  • graphical inference
  • message passing algorithm

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

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