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Identifying influential nodes in complex networks: A multiple attributes fusion method
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posted on 2014-01-01, 00:00 authored by L Zhong, C Gao, Zili ZhangZili Zhang, N Shi, J HuangHow to identify influential nodes is still an open hot issue in complex networks. Lots of methods (e.g., degree centrality, betweenness centrality or K-shell) are based on the topology of a network. These methods work well in scale-free networks. In order to design a universal method suitable for networks with different topologies, this paper proposes a Multiple Attribute Fusion (MAF) method through combining topological attributes and diffused attributes of a node together. Two fusion strategies have been proposed in this paper. One is based on the attribute union (FU), and the other is based on the attribute ranking (FR). Simulation results in the Susceptible-Infected (SI) model show that our proposed method gains more information propagation efficiency in different types of networks. © 2014 Springer International Publishing.
History
Title of book
Active Media TechnologyVolume
8610Series
Lecture Notes in Computer ScienceChapter number
2Pagination
11 - 22Publisher
SpringerPlace of publication
SwitzerlandPublisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319099125Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2014, SpringerExtent
47Editor/Contributor(s)
D Slezak, G Scahefer, S Vuong, Y KimUsage metrics
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