Analysis of Centrality Concepts Applied to Real-World Big Graph Data

Soyeon Oh, Kyeongjoo Kim, Minsoo Lee

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

Abstract

Graphs are mathematical models to represent relationships, and graph theories have an important role in recent research in the computer science area. These days, there are many kinds of graph-structured data such as social network service and biological and location data. And the graph data are now easily considered big data. Analyzing such graph data is an important problem. In this paper, we apply four major centralities and PageRank algorithms to real-world undirected graph data and find some empirical relationships and features of the algorithms. The results can be the starting point of many data-driven and theoretical link-based graph studies as well as social network service analysis.

Original languageEnglish
Title of host publicationAdvances in Computer Communication and Computational Sciences - Proceedings of IC4S 2018
EditorsKrishn K. Mishra, Sanjiv K. Bhatia, Munesh C. Trivedi, Shailesh Tiwari
PublisherSpringer Verlag
Pages619-627
Number of pages9
ISBN (Print)9789811368608
DOIs
StatePublished - 2019
EventInternational Conference on Computer, Communication and Computational Sciences, IC4S 2018 - Bangkok, Thailand
Duration: 20 Oct 201821 Oct 2018

Publication series

NameAdvances in Intelligent Systems and Computing
Volume924
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceInternational Conference on Computer, Communication and Computational Sciences, IC4S 2018
Country/TerritoryThailand
CityBangkok
Period20/10/1821/10/18

Keywords

  • Graph centrality
  • Graph mining
  • PageRank

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