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

Soyeon Oh; Kyeongjoo Kim; Minsoo Lee

doi:10.1007/978-981-13-6861-5_52

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

Soyeon Oh, Kyeongjoo Kim, Minsoo Lee

Computer Science & Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Advances in Computer Communication and Computational Sciences - Proceedings of IC4S 2018
Editors	Krishn K. Mishra, Sanjiv K. Bhatia, Munesh C. Trivedi, Shailesh Tiwari
Publisher	Springer Verlag
Pages	619-627
Number of pages	9
ISBN (Print)	9789811368608
DOIs	https://doi.org/10.1007/978-981-13-6861-5_52
State	Published - 2019
Event	International Conference on Computer, Communication and Computational Sciences, IC4S 2018 - Bangkok, Thailand Duration: 20 Oct 2018 → 21 Oct 2018

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	924
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	International Conference on Computer, Communication and Computational Sciences, IC4S 2018
Country/Territory	Thailand
City	Bangkok
Period	20/10/18 → 21/10/18

Keywords

Graph centrality
Graph mining
PageRank

Access to Document

10.1007/978-981-13-6861-5_52

Cite this

Oh, S., Kim, K., & Lee, M. (2019). Analysis of Centrality Concepts Applied to Real-World Big Graph Data. In K. K. Mishra, S. K. Bhatia, M. C. Trivedi, & S. Tiwari (Eds.), Advances in Computer Communication and Computational Sciences - Proceedings of IC4S 2018 (pp. 619-627). (Advances in Intelligent Systems and Computing; Vol. 924). Springer Verlag. https://doi.org/10.1007/978-981-13-6861-5_52

Oh, Soyeon ; Kim, Kyeongjoo ; Lee, Minsoo. / Analysis of Centrality Concepts Applied to Real-World Big Graph Data. Advances in Computer Communication and Computational Sciences - Proceedings of IC4S 2018. editor / Krishn K. Mishra ; Sanjiv K. Bhatia ; Munesh C. Trivedi ; Shailesh Tiwari. Springer Verlag, 2019. pp. 619-627 (Advances in Intelligent Systems and Computing).

@inproceedings{a323434f684f4b5893140e9c86f99be3,

title = "Analysis of Centrality Concepts Applied to Real-World Big Graph Data",

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.",

keywords = "Graph centrality, Graph mining, PageRank",

author = "Soyeon Oh and Kyeongjoo Kim and Minsoo Lee",

note = "Funding Information: Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1B03034691). This work was supported by the “Convergence Female Talent Education Project for Next Generation Industries” through the MSIP and NRF (2015H1C3A1064579). Funding Information: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1B03034691). This work was supported by the “Convergence Female Talent Education Project for Next Generation Industries” through the MSIP and NRF (2015H1C3A1064579). Publisher Copyright: {\textcopyright} 2019, Springer Nature Singapore Pte Ltd.; null ; Conference date: 20-10-2018 Through 21-10-2018",

year = "2019",

doi = "10.1007/978-981-13-6861-5_52",

language = "English",

isbn = "9789811368608",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "619--627",

editor = "Mishra, {Krishn K.} and Bhatia, {Sanjiv K.} and Trivedi, {Munesh C.} and Shailesh Tiwari",

booktitle = "Advances in Computer Communication and Computational Sciences - Proceedings of IC4S 2018",

}

Oh, S, Kim, K & Lee, M 2019, Analysis of Centrality Concepts Applied to Real-World Big Graph Data. in KK Mishra, SK Bhatia, MC Trivedi & S Tiwari (eds), Advances in Computer Communication and Computational Sciences - Proceedings of IC4S 2018. Advances in Intelligent Systems and Computing, vol. 924, Springer Verlag, pp. 619-627, International Conference on Computer, Communication and Computational Sciences, IC4S 2018, Bangkok, Thailand, 20/10/18. https://doi.org/10.1007/978-981-13-6861-5_52

Analysis of Centrality Concepts Applied to Real-World Big Graph Data. / Oh, Soyeon; Kim, Kyeongjoo; Lee, Minsoo.

Advances in Computer Communication and Computational Sciences - Proceedings of IC4S 2018. ed. / Krishn K. Mishra; Sanjiv K. Bhatia; Munesh C. Trivedi; Shailesh Tiwari. Springer Verlag, 2019. p. 619-627 (Advances in Intelligent Systems and Computing; Vol. 924).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Analysis of Centrality Concepts Applied to Real-World Big Graph Data

AU - Oh, Soyeon

AU - Kim, Kyeongjoo

AU - Lee, Minsoo

N1 - Funding Information: Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1B03034691). This work was supported by the “Convergence Female Talent Education Project for Next Generation Industries” through the MSIP and NRF (2015H1C3A1064579). Funding Information: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1B03034691). This work was supported by the “Convergence Female Talent Education Project for Next Generation Industries” through the MSIP and NRF (2015H1C3A1064579). Publisher Copyright: © 2019, Springer Nature Singapore Pte Ltd.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Graph centrality

KW - Graph mining

KW - PageRank

UR - http://www.scopus.com/inward/record.url?scp=85066146121&partnerID=8YFLogxK

U2 - 10.1007/978-981-13-6861-5_52

DO - 10.1007/978-981-13-6861-5_52

M3 - Conference contribution

AN - SCOPUS:85066146121

SN - 9789811368608

T3 - Advances in Intelligent Systems and Computing

SP - 619

EP - 627

BT - Advances in Computer Communication and Computational Sciences - Proceedings of IC4S 2018

A2 - Mishra, Krishn K.

A2 - Bhatia, Sanjiv K.

A2 - Trivedi, Munesh C.

A2 - Tiwari, Shailesh

PB - Springer Verlag

Y2 - 20 October 2018 through 21 October 2018

ER -

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

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Cite this