Abstract
Information diffusion prediction is an essential task in understanding the dissemination of information on social networks. Its objective is to predict the next user infected with a piece of information. While previous work focuses primarily on the analysis of diffusion sequences, recent work shifts towards examining social network connections between users. During the diffusion of information, users are expected to send and receive information. However, few works analyze the sending and receiving behavior of users during information diffusion. We design a Diffusion Simulation User Behavior Perception Attention Network (DSUBPAN). First, based on the social network graph, we construct a diffusion simulation heterogeneous network graph, which simulates diffusion, and obtain the sending and receiving behavior of users during information diffusion. Second, we utilize a user behavior fuse transformer to fuse different user behaviors. Then, we employ an attention network to perceive the time information and user sequence information in the information diffusion sequence. Finally, we utilize a dense layer and a softmax layer to predict the next infected user. Our model outperforms baseline methods on two real-world datasets, demonstrating its effectiveness.
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References
Barbieri, N., Bonchi, F., Manco, G.: Cascade-based community detection. In: WSDM, pp. 33–42 (2013)
Cao, Q., Shen, H., Cen, K., Ouyang, W., Cheng, X.: Deephawkes: bridging the gap between prediction and understanding of information cascades. In: CIKM, pp. 1149–1158 (2017)
Cao, Z., Han, K., Zhu, J.: Information diffusion prediction via dynamic graph neural networks. In: CSCWD, pp. 1099–1104. IEEE (2021)
Chen, X., Zhang, K., Zhou, F., Trajcevski, G., Zhong, T., Zhang, F.: Information cascades modeling via deep multi-task learning. In: SIGIR, pp. 885–888 (2019)
Gomez Rodriguez, M., Leskovec, J., Schölkopf, B.: Structure and dynamics of information pathways in online media. In: WSDM, pp. 23–32 (2013)
Hodas, N.O., Lerman, K.: The simple rules of social contagion. Sci. Rep. 4(1), 4343 (2014)
Islam, M.R., Muthiah, S., Adhikari, B., Prakash, B.A., Ramakrishnan, N.: Deepdiffuse: predicting the ‘who’ and ‘when’ in cascades. In: ICDM, pp. 1055–1060. IEEE (2018)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: SIGKDD, pp. 137–146 (2003)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. TWEB 1(1), 5-es (2007)
Rodriguez, M.G., Leskovec, J., Balduzzi, D., Schölkopf, B.: Uncovering the structure and temporal dynamics of information propagation. Netw. Sci. 2(1), 26–65 (2014)
Sankar, A., Zhang, X., Krishnan, A., Han, J.: Inf-vae: a variational autoencoder framework to integrate homophily and influence in diffusion prediction. In: WSDM, pp. 510–518 (2020)
Sun, L., Rao, Y., Zhang, X., Lan, Y., Yu, S.: MS-HGAT: memory-enhanced sequential hypergraph attention network for information diffusion prediction. In: AAAI, vol. 36, pp. 4156–4164 (2022)
Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)
Wallinga, J., Teunis, P.: Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am. J. Epidemiol. 160(6), 509–516 (2004)
Wang, J., Zheng, V.W., Liu, Z., Chang, K.C.C.: Topological recurrent neural network for diffusion prediction. In: ICDM, pp. 475–484. IEEE (2017)
Wang, Y., Shen, H., Liu, S., Gao, J., Cheng, X.: Cascade dynamics modeling with attention-based recurrent neural network. In: IJCAI, vol. 17, pp. 2985–2991 (2017)
Wang, Z., Chen, C., Li, W.: Attention network for information diffusion prediction. In: WWW, pp. 65–66 (2018)
Wang, Z., Chen, C., Li, W.: A sequential neural information diffusion model with structure attention. In: CIKM, pp. 1795–1798 (2018)
Wang, Z., Chen, C., Li, W.: Joint learning of user representation with diffusion sequence and network structure. TKDE 34(3), 1275–1287 (2020)
Wang, Z., Li, W.: Hierarchical diffusion attention network. In: IJCAI, pp. 3828–3834 (2019)
Yang, C., Sun, M., Liu, H., Han, S., Liu, Z., Luan, H.: Neural diffusion model for microscopic cascade prediction. arXiv preprint arXiv:1812.08933 (2018)
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)
Yang, J., Leskovec, J.: Modeling information diffusion in implicit networks. In: ICDM, pp. 599–608. IEEE (2010)
Yang, Y., et al.: Rain: social role-aware information diffusion. In: AAAI, vol. 29 (2015)
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. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12459, pp. 347–363. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67664-3_21
Zhang, Y., Lyu, T., Zhang, Y.: Cosine: community-preserving social network embedding from information diffusion cascades. In: AAAI, vol. 32 (2018)
Zhong, E., Fan, W., Wang, J., Xiao, L., Li, Y.: Comsoc: adaptive transfer of user behaviors over composite social network. In: SIGKDD, pp. 696–704 (2012)
Acknowledgment
This work was supported in part by the Joint Funds of the National Key Research and Development Program of China (2020YFB1406902) and the Fundamental Research Funds for the State Key Laboratory of Communication Content Cognition (A12003).
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Shao, Y., He, H., Tai, Y., Wu, X., Yang, H. (2024). A Diffusion Simulation User Behavior Perception Attention Network for Information Diffusion Prediction. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14433. Springer, Singapore. https://doi.org/10.1007/978-981-99-8546-3_15
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DOI: https://doi.org/10.1007/978-981-99-8546-3_15
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