Abstract
Identifying the most influential individuals spreading ideas, information, or infectious diseases is a topic receiving significant attention from network researchers, since such identification can assist or hinder information dissemination, product exposure, or contagious disease detection. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, few efforts have been made to use node diversity within network structures to measure spreading ability. The two-step framework described in this paper uses a robust and reliable measure that combines global diversity and local features to identify the most influential network nodes. Results from a series of Susceptible-Infected-Recovered (SIR) epidemic simulations indicate that our proposed method performs well and stably in single initial spreader scenarios associated with various complex network datasets.
Original language | English |
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Title of host publication | Proceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 |
Editors | Zhi-Hua Zhou, Wei Wang, Ravi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu |
Publisher | IEEE Computer Society |
Pages | 562-568 |
Number of pages | 7 |
Edition | January |
ISBN (Electronic) | 9781479942749 |
DOIs | |
State | Published - 26 01 2015 |
Event | 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 - Shenzhen, China Duration: 14 12 2014 → … |
Publication series
Name | IEEE International Conference on Data Mining Workshops, ICDMW |
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Number | January |
Volume | 2015-January |
ISSN (Print) | 2375-9232 |
ISSN (Electronic) | 2375-9259 |
Conference
Conference | 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 |
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Country/Territory | China |
City | Shenzhen |
Period | 14/12/14 → … |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
Keywords
- Epidemic model
- entropy
- k-shell decomposition
- network diversity
- social network analysis