Random Graph Node Classification by Extremal Index of PageRank

Natalia M. Markovich, Maxim S. Ryzhov

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Taking account for the graph randomness, our purpose is a node classification by their extremal indexes (EI) as the local dependence measure of node influence characteristics. The EI was calculated by node PageRanks of the local tree related to the node, which is a kind of Thorny Branching Tree (TBT). The blocks estimator was used for the EI estimation by sliding and disjoint block definitions. The classification by the node EI value and the average block size for the local node TBT was introduced for simulated graphs by the Forest Fire and Erdős-Rényi Models and the Berkeley-Stanford dataset as a real example. The new classification methodology is proposed irrespective on the graph structure.

Original languageEnglish
Title of host publicationDistributed Computer and Communication Networks - 22nd International Conference, DCCN 2019, Revised Selected Papers
EditorsVladimir M. Vishnevskiy, Dmitry V. Kozyrev, Konstantin E. Samouylov, Dmitry V. Kozyrev
PublisherSpringer
Pages424-435
Number of pages12
ISBN (Print)9783030366247
DOIs
StatePublished - 1 Jan 2019
Externally publishedYes
Event22nd International Conference on Distributed and Computer and Communication Networks, DCCN 2019 - Moscow, Russian Federation
Duration: 23 Sep 201927 Sep 2019

Publication series

NameCommunications in Computer and Information Science
Volume1141 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference22nd International Conference on Distributed and Computer and Communication Networks, DCCN 2019
Country/TerritoryRussian Federation
CityMoscow
Period23/09/1927/09/19

Keywords

  • Bootstrap
  • Disjoint block
  • Extremal index
  • PageRank
  • Random graph
  • Sliding block

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics

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