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An information theory based approach for identifying influential spreaders in temporal networks
conference contribution
posted on 2017-01-01, 00:00 authored by L Luo, L Tao, H Xu, Z Yuan, H Lai, Zili ZhangZili ZhangIdentifying the most influential nodes in computer networks is an important issue in preventing the spread of computer viruses. In order to quantify the importance of nodes in the spreading of computer viruses, various centrality measures have been developed under an assumption of a static network. These measures have limitations in that many network structures are dynamically change over time. In this paper, we extend an entropy-based centrality from time-independent networks to time-dependent networks by taking into account the temporal and spatial connections between different nodes simultaneously. We also propose an algorithm for ranking the influences of nodes. According to the experimental results on three synthetic networks and a real network for susceptible-infected-recovered (SIR) spreading model, our proposed temporal entropy-based centrality (TEC) is more accurate than existing temporal betweenness, and closeness centralities.
History
Event
Cyberspace safety and security. International symposium (9th : 2017 : Xi'an, China)Volume
10581Series
Lecture notes in computer sciencePagination
477 - 484Publisher
SpringerLocation
Xi'an, ChinaPlace of publication
Cham, SWitzerlandPublisher DOI
Start date
2017-10-23End date
2017-10-25ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319694702Language
engPublication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
2017, Springer International Publishing AG.Editor/Contributor(s)
Sheng Wen, Wei Wu, Aniello CastiglioneTitle of proceedings
CSS 2017 : Proceedings of the 9th International SymposiumUsage metrics
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