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Target Set Selection in Social Networks with Influence and Activation Thresholds

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Computational Data and Social Networks (CSoNet 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13116))

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Abstract

Social media networks have gradually become the major venue for broadcasting and relaying information, thereafter making great influences in many aspects of our daily lives. With the mass adoption of the internet and mobile devices, social media users tend to follow and adopt their friends’ or followers’ thoughts and behaviors. Thus finding influential users in social media is crucial for many viral marketing, cybersecurity, politics, and safety-related applications. In this study, we address the problem through solving the influence and activation thresholds target set selection problem, which is to find the minimum number of seed nodes that influence all the users at time T. These time-indexed integer program models suffer from computational difficulties with binary variables at each time step. To this respect, this paper leverages computational algorithms, i.e., Graph Partition, Nodes Selection, and Greedy Algorithm to solve the models for large-scale networks. Computational results show that it is beneficial to apply the BFS Greedy algorithm for large scale networks. In addition, the results also indicate nodes selection methods perform better in the long-tailed networks.

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, USA

    Zhecheng Qiang & Qipeng P. Zheng

  2. Munitions Directorate, Air Force Research Laboratory, Shalimar, FL, USA

    Eduardo L. Pasiliao

Authors
  1. Zhecheng Qiang
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  2. Eduardo L. Pasiliao
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  3. Qipeng P. Zheng
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Editor information

Editors and Affiliations

  1. University of Central Florida, Orlando, FL, USA

    David Mohaisen

  2. Kent State University, Kent, OH, USA

    Ruoming Jin

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Qiang, Z., Pasiliao, E.L., Zheng, Q.P. (2021). Target Set Selection in Social Networks with Influence and Activation Thresholds. In: Mohaisen, D., Jin, R. (eds) Computational Data and Social Networks. CSoNet 2021. Lecture Notes in Computer Science(), vol 13116. Springer, Cham. https://doi.org/10.1007/978-3-030-91434-9_32

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  • DOI: https://doi.org/10.1007/978-3-030-91434-9_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91433-2

  • Online ISBN: 978-3-030-91434-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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