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Measuring the influence and amplification of users on social network with unsupervised behaviors learning and efficient interaction-based knowledge graph

Author

Listed:
  • Quan M. Tran

    (Department of Research and Development, Kyanon Digital
    University of Information Technology
    Vietnam National University)

  • Hien D. Nguyen

    (University of Information Technology
    Vietnam National University)

  • Tai Huynh

    (Kyanon Digital)

  • Kha V. Nguyen

    (Department of Data Science, Kyanon Digital)

  • Suong N. Hoang

    (Olli Technology)

  • Vuong T. Pham

    (Sai Gon University)

Abstract

This study introduces a metric to measure the influence of users and communities on Social Media Networks. The proposed method is a combination of Knowledge Graph and Deep Learning approaches. Particularly, an effective Knowledge Graph is built to represent the interaction activities of users. Besides, an unsupervised deep learning model based on Variational Graph Autoencoder is also constructed to further learn and explore the behavior of users. This model is inspired by conventional Graph Convolutional layers. It is not only able to learn the attribute of users themselves but also enhanced to automatically extract and learn from the relationships among users. The model is robust to unseen data and takes no labeling effort. To ensure the state of the art and fashionable for this work, the dataset is collected by a designed crawling system. The experiments show significant performance and promising results which are competitive and outperforms some well-known Graph-convolutional-based. The proposed approach is applied to build a management system for an influencer marketing campaign, called ADVO system. The ADVO system can detect emerging influencers for a determined brand to run its campaign, and help the brand to manage its campaign. The proposed method is already applied in practice.

Suggested Citation

  • Quan M. Tran & Hien D. Nguyen & Tai Huynh & Kha V. Nguyen & Suong N. Hoang & Vuong T. Pham, 2022. "Measuring the influence and amplification of users on social network with unsupervised behaviors learning and efficient interaction-based knowledge graph," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2919-2945, November.
  • Handle: RePEc:spr:jcomop:v:44:y:2022:i:4:d:10.1007_s10878-021-00815-0
    DOI: 10.1007/s10878-021-00815-0
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    References listed on IDEAS

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    1. Tai Huynh & Hien Nguyen & Ivan Zelinka & Dac Dinh & Xuan Hau Pham, 2020. "Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †," Sustainability, MDPI, vol. 12(7), pages 1-16, April.
    2. Flaviano Morone & Hernán A. Makse, 2015. "Correction: Corrigendum: Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 527(7579), pages 544-544, November.
    3. Yuanjun Bi & Weili Wu & Yuqing Zhu & Lidan Fan & Ailian Wang, 2014. "A nature-inspired influence propagation model for the community expansion problem," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 513-528, October.
    4. Flaviano Morone & Hernán A. Makse, 2015. "Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 524(7563), pages 65-68, August.
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