Abstract
Identifying 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.
This work is supported by the Fundamental Research Funds for the Central Universities (No. XDJK2015C153 and SWU114112).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, J.X., Chen, D.B., Dong, Q., Zhao, Z.D.: Identifying a set of influential spreaders in complex networks. Sci. Rep. 6, 27823 (2016)
Lü, L., Chen, D., Ren, X., Zhang, Q., Zhang, Y., Zhou, T.: Vital nodes identification in complex networks. Phys. Rev. 650, 1–63 (2016)
Tang, J.K.: Temporal network metrics and their application to real world networks. Ph.D. dissertation, Univeristy of Cambridge (2011). https://www.cl.cam.ac.uk/~cm542/phds/johntang.pdf
Takaguchi, T., Yano, Y., Yoshida, Y.: Coverage centralities for temporal networks. Eur. Phys. J. B 89, 35 (2016)
Rocha, L.E.C., Masuda, N.: Random walk centrality for temporal networks. New J. Phys. 16, 063023 (2014)
Estrada, E.: Communicability in temporal networks. Phys. Rev. E 88, 042811 (2013)
Praprotnik, S., Batagelj, V.: Spectral centrality measures in temporal networks. Ars Math. Contemp. 11, 11 (2015)
Taylor, D., Myers, S.A., Clauset, A., Porter, M.A., Mucha, P.J.: Eigenvector-based centrality measures for temporal networks. Multiscale Model. Simul. 15(1), 537–574 (2017)
Alexander, G.N., Raihan, R., Ashwin, K.: On effcient use of entropy centrality for social network analysis and community detection. Soc. Netw. 40, 154–162 (2014)
Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948)
Newman, M.E.J., Strogatz, S.H., Watts, D.J.: Random graphs with arbitrary degree distributions and their applications. Phys. Rev. E 64(2), 026118 (2001)
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)
Barabási, A., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Luo, L., Tao, L., Xu, H., Yuan, Z., Lai, H., Zhang, Z. (2017). An Information Theory Based Approach for Identifying Influential Spreaders in Temporal Networks. In: Wen, S., Wu, W., Castiglione, A. (eds) Cyberspace Safety and Security. CSS 2017. Lecture Notes in Computer Science(), vol 10581. Springer, Cham. https://doi.org/10.1007/978-3-319-69471-9_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-69471-9_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69470-2
Online ISBN: 978-3-319-69471-9
eBook Packages: Computer ScienceComputer Science (R0)