Identifying Influential Nodes in Online Social Networks Using Principal Component Centrality

Muhammad U. Ilyas; Hayder Radha

doi:10.1109/icc.2011.5963147

Identifying Influential Nodes in Online Social Networks Using Principal Component Centrality

Muhammad U. Ilyas, Hayder Radha

Computer Science

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

Abstract

Identifying the most influential nodes in social networks is a key problem in social network analysis. However, without a strict definition of centrality the notion of what constitutes a central node in a network changes with application and the type of commodity flowing through a network. In this paper we identify social hubs, nodes at the center of influential neighborhoods, in massive online social networks using principal component centrality (PCC). We compare PCC with eigenvector centrality's (EVC), the de facto measure of node influence by virtue of their position in a network. We demonstrate PCC's performance by processing a friendship graph of 70, 000 users of Google's Orkut social networking service and a gaming graph of 143, 020 users obtained from users of Facebook's 'Fighters Club' application.

Original language	English
Title of host publication	2011 IEEE International Conference on Communications (ICC)
Publisher	IEEE
Pages	1-5
Number of pages	5
ISBN (Print)	978-1-61284-231-8
DOIs	https://doi.org/10.1109/icc.2011.5963147
Publication status	Published - 9 Jun 2011
Event	2011 IEEE International Conference on Communications (ICC) - Kyoto, Japan Duration: 5 Jun 2011 → 9 Jun 2011

Conference

Conference	2011 IEEE International Conference on Communications (ICC)
Period	5/06/11 → 9/06/11

Keywords

Peer to peer computing
Social network services
Histograms
Eigenvalues and eigenfunctions
Monitoring
Google
IEEE Communications Society

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10.1109/icc.2011.5963147

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5963147

Cite this

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title = "Identifying Influential Nodes in Online Social Networks Using Principal Component Centrality",

abstract = "Identifying the most influential nodes in social networks is a key problem in social network analysis. However, without a strict definition of centrality the notion of what constitutes a central node in a network changes with application and the type of commodity flowing through a network. In this paper we identify social hubs, nodes at the center of influential neighborhoods, in massive online social networks using principal component centrality (PCC). We compare PCC with eigenvector centrality's (EVC), the de facto measure of node influence by virtue of their position in a network. We demonstrate PCC's performance by processing a friendship graph of 70, 000 users of Google's Orkut social networking service and a gaming graph of 143, 020 users obtained from users of Facebook's 'Fighters Club' application.",

keywords = "Peer to peer computing, Social network services, Histograms, Eigenvalues and eigenfunctions, Monitoring, Google, IEEE Communications Society",

author = "Ilyas, {Muhammad U.} and Hayder Radha",

year = "2011",

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doi = "10.1109/icc.2011.5963147",

language = "English",

isbn = "978-1-61284-231-8",

pages = "1--5",

booktitle = "2011 IEEE International Conference on Communications (ICC)",

publisher = "IEEE",

note = "2011 IEEE International Conference on Communications (ICC) ; Conference date: 05-06-2011 Through 09-06-2011",

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T1 - Identifying Influential Nodes in Online Social Networks Using Principal Component Centrality

AU - Ilyas, Muhammad U.

AU - Radha, Hayder

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N2 - Identifying the most influential nodes in social networks is a key problem in social network analysis. However, without a strict definition of centrality the notion of what constitutes a central node in a network changes with application and the type of commodity flowing through a network. In this paper we identify social hubs, nodes at the center of influential neighborhoods, in massive online social networks using principal component centrality (PCC). We compare PCC with eigenvector centrality's (EVC), the de facto measure of node influence by virtue of their position in a network. We demonstrate PCC's performance by processing a friendship graph of 70, 000 users of Google's Orkut social networking service and a gaming graph of 143, 020 users obtained from users of Facebook's 'Fighters Club' application.

AB - Identifying the most influential nodes in social networks is a key problem in social network analysis. However, without a strict definition of centrality the notion of what constitutes a central node in a network changes with application and the type of commodity flowing through a network. In this paper we identify social hubs, nodes at the center of influential neighborhoods, in massive online social networks using principal component centrality (PCC). We compare PCC with eigenvector centrality's (EVC), the de facto measure of node influence by virtue of their position in a network. We demonstrate PCC's performance by processing a friendship graph of 70, 000 users of Google's Orkut social networking service and a gaming graph of 143, 020 users obtained from users of Facebook's 'Fighters Club' application.

KW - Peer to peer computing

KW - Social network services

KW - Histograms

KW - Eigenvalues and eigenfunctions

KW - Monitoring

KW - Google

KW - IEEE Communications Society

UR - https://ieeexplore.ieee.org/document/5963147/

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DO - 10.1109/icc.2011.5963147

M3 - Conference contribution

SN - 978-1-61284-231-8

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EP - 5

BT - 2011 IEEE International Conference on Communications (ICC)

PB - IEEE

T2 - 2011 IEEE International Conference on Communications (ICC)

Y2 - 5 June 2011 through 9 June 2011

ER -

Identifying Influential Nodes in Online Social Networks Using Principal Component Centrality

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Keywords

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