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2018 | OriginalPaper | Chapter

Multi-diffusion Degree Centrality Measure to Maximize the Influence Spread in the Multilayer Social Networks

Authors : Ibrahima Gaye, Gervais Mendy, Samuel Ouya, Idy Diop, Diaraf Seck

Published in: e-Infrastructure and e-Services for Developing Countries

Publisher: Springer International Publishing

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Abstract

In this work, we study the influence maximization in multilayer social networks. This problem is to find a set of k persons, called seeds, that maximizes the information spread in a multilayer social network. In our works, we focus in the determination of the seeds by proposing a centrality measure called Multi-Diffusion Degree (denoted by \(C_{dd}^{MLN}\)) based on Independent Cascade model. We consider the \(top-K\) persons as the most influential. This centrality measure uses firstly, the diffusion probability for each person in each layer. Secondly, it uses the contribution of the first neighbors in the diffusion process. To show the performance of our approach, we compare it with the existing heuristics like multi degree centrality. With software R and igraph package, we show that Multi-Diffusion Degree is more performant than the benchmark heuristic.

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Footnotes
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Metadata
Title
Multi-diffusion Degree Centrality Measure to Maximize the Influence Spread in the Multilayer Social Networks
Authors
Ibrahima Gaye
Gervais Mendy
Samuel Ouya
Idy Diop
Diaraf Seck
Copyright Year
2018
Book
e-Infrastructure and e-Services for Developing Countries

Print ISBN: 978-3-319-66741-6

Electronic ISBN: 978-3-319-66742-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-66742-3_6

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