Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Anand, Garima; Srivastava, Shilpa; Agarwal, Ritu Title A name given to the resource Modeling Popularity Evolution with Popularity-Augmented Graphs and Dynamic Bayesian PARAFAC Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 5th International Conference on Advancement in Electronics and Communication Engineering, AECE 2025;pp.679-684 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/AECE67531.2025.11386527" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AECE67531.2025.11386527</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035203193?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035203193?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Dynamic Bayesian Tensor Filter; Graph Partitioning; Prediction; Social Media Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155296-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online