eISSN: 3087-4289 editor@aimjournals.com
All Journals
Submit your article

International Journal of Modern Computer Science and IT Innovations

Open Access Peer Review International
Open Access

LEVERAGING PERSISTENCE AND GRAPH NEURAL NETWORKS FOR ENHANCED INFORMATION POPULARITY FORECASTING

DOI https://doi.org/10.55640/ijmcsit-v02i04-01
pdf
Dr. Erik G. Johansson 1 , Dr. Linnea K. Blomqvist 1 ,
4 Department of Computer and Information Science, Linköping University, Sweden
4 Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden

Abstract

Accurately forecasting the popularity of online information is critical for optimizing content delivery, recommendation systems, and network resource allocation. This paper introduces a novel framework that leverages temporal persistence patterns and graph neural networks (GNNs) to improve the prediction of information popularity. By modeling user-content interactions as dynamic graphs and incorporating historical popularity trends, our approach captures both structural and temporal dependencies. Extensive experiments on real-world social and content-sharing platforms demonstrate that the proposed method significantly outperforms traditional forecasting models in terms of accuracy and robustness. The results highlight the potential of combining graph-based learning with temporal analysis for intelligent information propagation modeling.

Keywords

Information popularity forecasting, graph neural networks, temporal persistence, dynamic graphs

References

📄 F. Zhou, X. Xu, G. Trajcevski, and K. Zhang, “A survey of information cascade analysis: Models, predictions, and recent advances,” ACM Comput. Surv., vol. 54, no. 2, pp. 1–36, 2021.
📄 R. A. Ibrahim, H. A. Hefny, and A. E. Hassanien, “Controlling social information cascade: A Survey,” in Big Data Analytics, 1st ed. CRC Press, 2018, pp. 196–212.
📄 A. Guille, H. Hacid, C. Favre, and D. A. Zighed, “Information diffusion in online social networks: A survey,” ACM Sigmod Rec., vol. 42, no. 2, pp. 17–28, 2013.
📄 T. R. Zaman, R. Herbrich, J. Van Gael, and D. Stern, “Predicting information spreading in twitter,” in Proc. Workshop Comput. Social Sci. Wisdom Crowds, vol. 104, no. 45. 2010, pp. 17599–601.
📄 K. Lee, J. Mahmud, J. Chen, M. Zhou, and J. Nichols, “Who will retweet this? automatically identifying and engaging strangers on twitter to spread information,” in Proc. 19th Int. Conf. Intell. User Interfaces, 2014, pp. 247–256.
📄 Z. Luo, M. Osborne, J. Tang, and T. Wang, “Who will retweet me? finding retweeters in twitter,” in Proc. 36th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2013, pp. 869–872.
📄 T. Zaman, E. B. Fox, and E. T. Bradlow, “A Bayesian approach for predicting the popularity of tweets,” Ann. Appl. Statist., vol. 8, no. 3, pp. 1583–1611, 2014.
📄 S. Jamali and H. Rangwala, “Digging digg: Comment mining, popularity prediction, and social network analysis,” in Proc. IEEE Int. Conf. Web Inf. Syst. Mining, 2009, pp. 32–38.
📄 P. Bao, H.-W. Shen, J. Huang, and X.-Q. Cheng, “Popularity prediction in microblogging network: A case study on sina weibo,” in Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 177–178.
📄 J. Chen, X. Song, L. Nie, X. Wang, H. Zhang, and T.-S. Chua, “Micro tells macro: Predicting the popularity of micro-videos via a transductive model,” in Proc. 24th ACM Int. Conf. Multimedia, 2016, pp. 898–907.
📄 M. X. Hoang, X.-H. Dang, X. Wu, Z. Yan, and A. K. Singh, “GPOP: Scalable group-level popularity prediction for online content in social networks,” in Proc. 26th Int. Conf. World Wide Web, 2017, pp. 725–733.
📄 J. Lv, W. Liu, M. Zhang, H. Gong, B. Wu, and H. Ma, “Multi-feature fusion for predicting social media popularity,” in Proc. 25th ACM Int. Conf. Multimedia, 2017, pp. 1883–1888.
📄 B. Wu, T. Mei, W.-H. Cheng, and Y. Zhang, “Unfolding temporal dynamics: Predicting social media popularity using multi-scale temporal decomposition,” in Proc. 13th AAAI Conf. Artif. Intell., 2016, pp. 272–278.
📄 S. Gao, J. Ma, and Z. Chen, “Effective and effortless features for popularity prediction in microblogging network,” in Proc. 23rd Int. Conf. World Wide Web, 2014, pp. 269–270.
📄 C. Yi, Y. Bao, and Y. Xue, “Mining the key predictors for event outbreaks in social networks,” Physica A: Stat. Mechanics Appl., vol. 447, pp. 247–260, 2016.
📄 S. Bourigault, S. Lamprier, and P. Gallinari, “Representation learning for information diffusion through social networks: An embedded cascade model,” in Proc. 9th ACM Int. Conf. Web Search Data Mining, 2016, pp. 573–582.
📄 Q. Kong, M.-A. Rizoiu, and L. Xie, “Modeling information cascades with self-exciting processes via generalized epidemic models,” in Proc. 13th Int. Conf. Web Search Data Mining, 2020, pp. 286–294.
📄 L. Yu, P. Cui, F. Wang, C. Song, and S. Yang, “Uncovering and predicting the dynamic process of information cascades with survival model,” Knowl. Inf. Syst., vol. 50, no. 2, pp. 633–659, 2017.
📄 Q. Zhao, M. A. Erdogdu, H. Y. He, A. Rajaraman, and J. Leskovec, “Seismic: A self-exciting point process model for predicting tweet popularity,” in Proc. 21th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2015, pp. 1513–1522.
📄 H. Shen, D. Wang, C. Song, and A.-L. Barabási, “Modeling and predicting popularity dynamics via reinforced poisson processes,” in Proc. AAAI Conf. Artif. Intell., vol. 28, no. 1, pp. 291–297, 2014.
📄 N. Garg, M. Sellathurai, V. Bhatia, B. N. Bharath, and T. Ratnarajah, “Online content popularity prediction and learning in wireless edge caching,” IEEE Trans. Commun., vol. 68, no. 2, pp. 1087–1100, Feb. 2020.
📄 G. Ver Steeg and A. Galstyan, “Information-theoretic measures of influence based on content dynamics,” in Proc. 6th ACM Int. Conf. Web Search Data Mining, 2013, pp. 3–12.
📄 Q. Cao, H. Shen, K. Cen, W. Ouyang, and X. Cheng, “Deephawkes: Bridging the gap between prediction and understanding of information cascades,” in Proc. 2017 ACM Conf. Inf. Knowl. Manage., 2017, pp. 1149–1158.
📄 J. Wang, V. W. Zheng, Z. Liu, and K. C.-C. Chang, “Topological recurrent neural network for diffusion prediction,” in Proc. IEEE Int. Conf. Data Mining, 2017, pp. 475–484.
📄 X. Chen, F. Zhou, K. Zhang, G. Trajcevski, T. Zhong, and F. Zhang, “Information diffusion prediction via recurrent cascades convolution,” in Proc. IEEE 35th Int. Conf. Data Eng., 2019, pp. 770–781.
📄 C. Li, J. Ma, X. Guo, and Q. Mei, “DeepCas: An end-to-end predictor of information cascades,” in Proc. 26th Int. Conf. World Wide Web, 2017, pp. 577–586.
📄 Y. Wang, X. Wang, R. Michalski, Y. Ran, and T. Jia, “CasSeqGCN: Combining network structure and temporal sequence to predict information cascades,” Expert Syst. Appl., vol. 206, Nov., 2022, Art. no. 117693, doi: 10.1016/j.eswa.2022.117693.
📄 M. Wang and K. Li, “Predicting information diffusion cascades using graph attention networks,” in Proc. Int. Conf. Neural Inf. Process., 2020, pp. 104–112.
📄 Z. Huang, Z. Wang, R. Zhang, Y. Zhao, and F. Zheng, “Learning Bi-directional social influence in information cascades using graph sequence attention networks,” in Proc. Companion Web Conf., 2020, pp. 19–21.
📄 R. Wang, “Dydiff-VAE: A dynamic variational framework for information diffusion prediction,” in Proc. 44th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2021, pp. 163–172.

Similar Articles

21-28 of 28

You may also start an advanced similarity search for this article.