Modeling Popularity Evolution with Popularity-Augmented Graphs and Dynamic Bayesian PARAFAC
subject: Dynamic Bayesian Tensor Filter; Graph Partitioning; Prediction; Social Media
description: In recent years, social media has evolved as a significant platform for attracting new clients and customers. Every day, a wide range of new offers and products are shared over the social media platforms for buying, selling, promotions, etc., encouraging more and more social engagement. Therefore, it's important to predict high consumer engagement using past interactions. This study proposes a two-stage framework that integrates Popularity Augmented Social Graph construction with Dynamic Bayesian PARAFAC decomposition. The experiments were conducted on the open-source Behance project dataset, which contains interactions from over 85,000 users across 1,326 projects over 60 discrete time intervals. In the first stage, a Popularity Augmented Social Graph (PASG) is constructed using the popularity information. In the second stage, the graph is represented in tensor form and is factorized using Dynamic Bayesian PARAFAC (DBPF), which models latent relationships across users, content, and time. The performance of the model was evaluated using Mean Relative Error, Mean Absolute Error, Root Mean Squared Error, where it consistently outperformed the baseline methods. The results demonstrate the effectiveness of the proposed framework in providing a robust and scalable solution for popularity prediction in social media platforms. 2025 IEEE.
creator: Anand, Garima; Srivastava, Shilpa; Agarwal, Ritu
source: 2025 5th International Conference on Advancement in Electronics and Communication Engineering, AECE 2025;pp.679-684
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833155296-1;
language: English
type: Conference paper
coverage: Anand G., Christ University, School of Sciences, Bengaluru, India; Srivastava S., Christ University, School of Sciences, Bengaluru, India; Agarwal R., Raj Kumar Goel Institute of Technology, Department of Computer Science, Ghaziabad, India