A Centrality Measure for Influence Maximization Across Multiple Social Networks

Influence maximization (IM) is the problem of sub set selection which selects a subset of k users from the network to maximize the aggregate influence spread in the network. The paper addresses IM problem across multiple social networks simultaneously. We propose a new centrality measure to identify...

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Bibliographic Details
Published inAdvanced Informatics for Computing Research Vol. 1076; pp. 195 - 207
Main Authors Singh, Shashank Sheshar, Kumar, Ajay, Mishra, Shivansh, Singh, Kuldeep, Biswas, Bhaskar
Format Book Chapter
LanguageEnglish
Published Singapore Springer 2019
Springer Singapore
SeriesCommunications in Computer and Information Science
Subjects
Influence maximization
Information diffusion
Social influence
Social networks
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ISBN9811501106
9789811501104
ISSN1865-0929
1865-0937
DOI10.1007/978-981-15-0111-1_18

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Summary:Influence maximization (IM) is the problem of sub set selection which selects a subset of k users from the network to maximize the aggregate influence spread in the network. The paper addresses IM problem across multiple social networks simultaneously. We propose a new centrality measure to identify the most influential users and adopt the independent cascade model for information dissemination. The experiment results show the advantage of the proposed framework over classical influence maximization frameworks. The results also show the superiority of the proposed centrality measure over the state-of-the-art centrality measures.
ISBN:9811501106
9789811501104
ISSN:1865-0929
1865-0937
DOI:10.1007/978-981-15-0111-1_18