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

Quan M. Tran (), Hien D. Nguyen (), Tai Huynh (), Kha V. Nguyen (), Suong N. Hoang () and Vuong T. Pham ()
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Quan M. Tran: Department of Research and Development, Kyanon Digital
Hien D. Nguyen: University of Information Technology
Tai Huynh: Kyanon Digital
Kha V. Nguyen: Department of Data Science, Kyanon Digital
Suong N. Hoang: Olli Technology
Vuong T. Pham: Sai Gon University

Journal of Combinatorial Optimization, 2022, vol. 44, issue 4, No 39, 2919-2945

Abstract: 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.

Keywords: Knowledge graph; Node embedding; Social network analysis; Variational auto-encoder; Unsupervised learning; Graph convolutional network; Deep learning; Influencer marketing; Information propagation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10878-021-00815-0

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