Using global diversity and local features to identify influential social network spreaders

Yu Hsiang Fu; Chung Yuan Huang; Chuen Tsai Sun

doi:10.1109/ASONAM.2014.6921700

Using global diversity and local features to identify influential social network spreaders

Yu Hsiang Fu, Chung Yuan Huang, Chuen Tsai Sun

Department of Computer Science and Information Engineering

National Yang Ming Chiao Tung University

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

6 Scopus citations

Abstract

The identification of influential spreaders of information via social networks can assist in the acceleration or hindrance of information dissemination, in increased product exposure, and in the detection of contagious disease outbreaks. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, researchers have overlooked node diversity within network structures as a means of measuring spreading ability. The two-step framework described in this paper uses a robust and insensitive measure that combines global diversity and local features (e.g., degree centrality) to identify the most influential social network nodes. Preliminary experiment results indicate that the proposed method performs well and maintains stability in single initial spreader scenarios associated with different social network datasets.

Original language	English
Title of host publication	ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Editors	Xindong Wu, Xindong Wu, Martin Ester, Guandong Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	948-953
Number of pages	6
ISBN (Electronic)	9781479958771
DOIs	https://doi.org/10.1109/ASONAM.2014.6921700
State	Published - 10 10 2014
Event	2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014 - Beijing, China Duration: 17 08 2014 → 20 08 2014

Publication series

Name	ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Conference

Conference	2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014
Country/Territory	China
City	Beijing
Period	17/08/14 → 20/08/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

entropy
epidemic model
k-shell decomposition
network diversity
social network analysis

Access to Document

10.1109/ASONAM.2014.6921700

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Fu, Y. H., Huang, C. Y., & Sun, C. T. (2014). Using global diversity and local features to identify influential social network spreaders. In X. Wu, X. Wu, M. Ester, & G. Xu (Eds.), ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 948-953). Article 6921700 (ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM.2014.6921700

Fu, Yu Hsiang ; Huang, Chung Yuan ; Sun, Chuen Tsai. / Using global diversity and local features to identify influential social network spreaders. ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. editor / Xindong Wu ; Xindong Wu ; Martin Ester ; Guandong Xu. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 948-953 (ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining).

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abstract = "The identification of influential spreaders of information via social networks can assist in the acceleration or hindrance of information dissemination, in increased product exposure, and in the detection of contagious disease outbreaks. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, researchers have overlooked node diversity within network structures as a means of measuring spreading ability. The two-step framework described in this paper uses a robust and insensitive measure that combines global diversity and local features (e.g., degree centrality) to identify the most influential social network nodes. Preliminary experiment results indicate that the proposed method performs well and maintains stability in single initial spreader scenarios associated with different social network datasets.",

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Fu, YH, Huang, CY & Sun, CT 2014, Using global diversity and local features to identify influential social network spreaders. in X Wu, X Wu, M Ester & G Xu (eds), ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining., 6921700, ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Institute of Electrical and Electronics Engineers Inc., pp. 948-953, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014, Beijing, China, 17/08/14. https://doi.org/10.1109/ASONAM.2014.6921700

Using global diversity and local features to identify influential social network spreaders. / Fu, Yu Hsiang; Huang, Chung Yuan; Sun, Chuen Tsai.
ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ed. / Xindong Wu; Xindong Wu; Martin Ester; Guandong Xu. Institute of Electrical and Electronics Engineers Inc., 2014. p. 948-953 6921700 (ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining).

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

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AU - Huang, Chung Yuan

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N2 - The identification of influential spreaders of information via social networks can assist in the acceleration or hindrance of information dissemination, in increased product exposure, and in the detection of contagious disease outbreaks. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, researchers have overlooked node diversity within network structures as a means of measuring spreading ability. The two-step framework described in this paper uses a robust and insensitive measure that combines global diversity and local features (e.g., degree centrality) to identify the most influential social network nodes. Preliminary experiment results indicate that the proposed method performs well and maintains stability in single initial spreader scenarios associated with different social network datasets.

AB - The identification of influential spreaders of information via social networks can assist in the acceleration or hindrance of information dissemination, in increased product exposure, and in the detection of contagious disease outbreaks. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, researchers have overlooked node diversity within network structures as a means of measuring spreading ability. The two-step framework described in this paper uses a robust and insensitive measure that combines global diversity and local features (e.g., degree centrality) to identify the most influential social network nodes. Preliminary experiment results indicate that the proposed method performs well and maintains stability in single initial spreader scenarios associated with different social network datasets.

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Fu YH, Huang CY, Sun CT. Using global diversity and local features to identify influential social network spreaders. In Wu X, Wu X, Ester M, Xu G, editors, ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Institute of Electrical and Electronics Engineers Inc. 2014. p. 948-953. 6921700. (ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining). doi: 10.1109/ASONAM.2014.6921700

Using global diversity and local features to identify influential social network spreaders

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Keywords

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