Abstract
In recent years, with the prosperity of online social media platforms, cascade popularity prediction has attracted much attention from both academia and industry. Due to the recent advance in graph representation learning technologies, many state-of-the-art prediction methods utilize graph neural network to predict the cascade popularity. However, a significant disadvantage shared by these methods is that they treat each cascade independently, while the collaborations among different cascades are ignored. Therefore, in this paper we propose a novel deep learning model CollaborateCas which utilizes collaborations among different cascades to learn node and cascade embeddings directly and simultaneously. To this end, we first construct a heterogeneous user-message bipartite graph where different cascades are indirectly connected by common participants. To further capture temporal interdependence among users within each cascade, we construct homogeneous cascade graphs where temporal information is modeled as edge features. Experimental results on two real-world datasets show that our approach achieves significantly higher prediction accuracy compared with state-of-the-art approaches.
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References
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)
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)
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, pp. 70–78 (2020)
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. IEEE (2019)
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. Adv. Neural Inf. Process. Syst. 29, 3844–3852 (2016)
Feng, X., Zhao, Q., Liu, Z.: Prediction of information cascades via content and structure proximity preserved graph level embedding. Inf. Sci. 560, 424–440 (2021)
Gehrke J, Ginsparg P, K.J.: Overview of the 2003 KDD cup. In: Acm Sigkdd Explor. Newslett. 5(2), 149–151 (2003)
Gong, Q., et al.: Cross-site prediction on social influence for cold-start users in online social networks. ACM Trans. Web (TWEB) 15(2), 1–23 (2021)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)
Jiang, B., Lu, Z., Li, N., Wu, J., Yi, F., Han, D.: Retweeting prediction using matrix factorization with binomial distribution and contextual information. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) DASFAA 2019. LNCS, vol. 11447, pp. 121–138. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18579-4_8
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, pp. 577–586 (2017)
Myers, S.A., Leskovec, J.: Clash of the contagions: cooperation and competition in information diffusion. In: 2012 IEEE 12th International Conference on Data Mining, pp. 539–548. IEEE (2012)
Su, Y., Zhang, X., Wang, S., Fang, B., Zhang, T., Yu, P.S.: Understanding information diffusion via heterogeneous information network embeddings. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) DASFAA 2019. LNCS, vol. 11446, pp. 501–516. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18576-3_30
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)
Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018)
Weng, L., Flammini, A., Vespignani, A., Menczer, F.: Competition among memes in a world with limited attention. Sci. Rep. 2(1), 1–9 (2012)
Zhou, F., Xu, X., Trajcevski, G., Zhang, K.: A survey of information cascade analysis: models, predictions, and recent advances. ACM Comput. Surv. (CSUR) 54(2), 1–36 (2021)
Acknowledgements
This work was supported in part by: National Natural Science Foundation of China (Nos. 61966008, U2033213, 61804017).
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Zhang, X., Shang, J., Jia, X., Liu, D., Hao, F., Zhang, Z. (2022). CollaborateCas: Popularity Prediction of Information Cascades Based on Collaborative Graph Attention Networks. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13245. Springer, Cham. https://doi.org/10.1007/978-3-031-00123-9_56
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