Computing Amplification Factors for Influence on Social Network based on Learning of Behaviors and Interacted Knowledge Graph

Hien D. Nguyen, Quan M. Tran, Vuong T. Pham, Tai Huynh

Abstract: This presentation proposes a metric to estimate the influence of users and communities on Social Media Networks through the combining of Knowledge Graph and Deep Learning approaches. An unsupervised deep learning model is constructed to learn and explore the behavior of users based on Variational Graph Autoencoder (VGAE). It enhances to automatically extract from the relationships among users. The model is robust to unseen data and takes no labeling effort. 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 measure the influence of users on the practical social network. This sudy also presents an architecture of a system to mange campaigns of digital marketing on social network.

External IDs:dblp:conf/hpcc/NguyenTPH21