Abstract
Understanding information cascades in social networks is a critical research area with implications in various domains, such as viral marketing, opinion formation, and misinformation propagation. In information cascade prediction problem, one of the most important factors is the cascade structure of the social network, which can be described as a cascade graph, global graph, or an r-reachable graph. However, the majority of existing studies primarily focus on a singular type of relationship within the social network, relying on the homogeneous graph neural network. We introduce two novel approaches for heterogeneous social network cascading and analyze whether heterogeneous social networks have higher predictive accuracy than homogeneous networks, taking into account the potential differential effects of temporal sequences on the models. Further, our work highlights that the selection of edge types plays an important role in the accuracy of predicting information cascades within social networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bikhchandani, S., Hirshleifer, D., Welch, I.: A theory of fads, fashion, custom, and cultural change as informational cascades. J. Polit. Econ. 100, 992–1026 (1992)
Subramani, M.R., Rajagopalan, B.: Knowledge-sharing and influence in online social networks via viral marketing. Commun. ACM 46(12), 300–307 (2003)
Wang, Y., Wang, X., Ran, Y., Michalski, R., Jia, T.: CasSeqGCN: combining network structure and temporal sequence to predict information cascades. Exp. Syst. Appl. 206(C) (2022). https://doi.org/10.1016/j.eswa.2022.117693
Wu, Q., Gao, Y., Gao, X., Weng, P., Chen, G.: Dual sequential prediction models linking sequential recommendation and information dissemination. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 447–457 (2019)
Wang, S., Zhou, L., Kong, B.: Information cascade prediction based on T-DeepHawkes model. IOP Conf. Ser. Mater. Sci. Eng. 715(1), 012042 (2020). https://doi.org/10.1088/1757-899X/715/1/012042
Yang, C., Sun, M., Liu, H., Han, S., Liu, Z., Luan, H.: Neural diffusion model for microscopic cascade study. IEEE Trans. Knowl. Data Eng. 33(3), 1128–1139 (2019)
Yang, C., Tang, J., Sun, M., Cui, G., Liu, Z.: Multi-scale information diffusion prediction with reinforced recurrent networks. In: IJCAI, pp. 4033–4039 (2019)
Cao, Q., Shen, H., Cen, K., Ouyang, W., Cheng, X.: DeepHawkes: bridging the gap between prediction and understanding of information cascades. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1149–1158 (2017)
Jenders, M., Kasneci, G., Naumann, F.: Analyzing and predicting viral tweets. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 657–664 (2013)
Kong, S., Mei, Q., Feng, L., Ye, F., Zhao, Z.: Predicting bursts and popularity of hashtags in real-time. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2014, pp. 927–930. Association for Computing Machinery, New York (2014). https://doi.org/10.1145/2600428.2609476
Bo, H., McConville, R., Hong, J., Liu, W.: Social influence prediction with train and test time augmentation for graph neural networks. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2021)
Yuan, C., Li, J., Zhou, W., Lu, Y., Zhang, X., Hu, S.: DyHGCN: a dynamic heterogeneous graph convolutional network to learn users’ dynamic preferences for information diffusion prediction. arXiv arXiv:2006.05169 (2020). https://api.semanticscholar.org/CorpusID:219559005
Nielsen, D.S., McConville, R.: MuMiN: a large-scale multilingual multimodal fact-checked misinformation social network dataset. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3141–3153 (2022)
Zhou, F., Xu, X., Trajcevski, G., Zhang, K.: A survey of information cascade analysis: models, predictions, and recent advances. ACM Comput. Surv. 54(2), 1–36 (2021). https://doi.org/10.1145/3433000
Petrovic, S., Osborne, M., Lavrenko, V.: RT to win! Predicting message propagation in Twitter. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 5, no. 1, pp. 586–589 (2011)
Kamath, K.Y., Caverlee, J.: Spatio-temporal meme prediction: learning what hashtags will be popular where. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management (2013). https://api.semanticscholar.org/CorpusID:2062983
Wang, D., Song, C., Barabási, A.-L.: Quantifying long-term scientific impact. Science 342(6154), 127–132 (2013)
Hassan Zadeh, A., Sharda, R.: Modeling brand post popularity dynamics in online social networks. Decis. Support Syst. 65, 59–68 (2014). https://www.sciencedirect.com/science/article/pii/S0167923614001432
Kobayashi, R., Lambiotte, R.: TiDeH: time-dependent Hawkes process for predicting retweet dynamics. In: Tenth International AAAI Conference on Web and Social Media (2016)
Li, C., Ma, J., Guo, X., Mei, Q.: DeepCas: an end-to-end predictor of information cascades. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, pp. 577–586. International World Wide Web Conferences Steering Committee, CHE, Republic and Canton of Geneva (2017). https://doi.org/10.1145/3038912.3052643
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
Chen, X., Zhou, F., Zhang, K., Trajcevski, G., Zhong, T., Zhang, F.: Information diffusion prediction via recurrent cascades convolution. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 770–781 (2019)
Wu, Y., Huang, H., Jin, H.: Information diffusion prediction with personalized graph neural networks. In: Li, G., Shen, H.T., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds.) KSEM 2020, Part II. LNCS (LNAI), vol. 12275, pp. 376–387. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-55393-7_34
Chen, X., Zhang, F., Zhou, F., Bonsangue, M.: Multi-scale graph capsule with influence attention for information cascades prediction. Int. J. Intell. Syst. 37(3), 2584–2611 (2022)
Cao, Q., Shen, H., Gao, J., Wei, B, Cheng, X.: Popularity prediction on social platforms with coupled graph neural networks. In: Proceedings of the 13th International Conference on Web Search and Data Mining (2019). https://api.semanticscholar.org/CorpusID:208309901
Huang, Z., Wang, Z., Zhang, R.: Cascade2vec: learning dynamic cascade representation by recurrent graph neural networks. IEEE Access 7, 144 800–144 812 (2019)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Bozinovski, S.: Reminder of the first paper on transfer learning in neural networks, 1976. Informatica 44(3) (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, Y., McAreavey, K., Liu, W., McConville, R. (2024). A Comparative Analysis of Information Cascade Prediction Using Dynamic Heterogeneous and Homogeneous Graphs. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-031-53503-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-53503-1_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53502-4
Online ISBN: 978-3-031-53503-1
eBook Packages: EngineeringEngineering (R0)