Abstract
The spread of events happens all the time in social networks. The prediction of event propagation has received extensive attention in data mining community. In prior studies, topologies in social networks are usually exploited to predict the scope of event propagation. User’s action logs can be obtained in reality, but it is difficult to get topologies in social networks. In this paper, NT-GP, a prediction model for non-topological event propagation, is proposed. Firstly a time decay sampling method was used to extract the walk paths from user’s action log, and then deep learning method was applied to learn the sampling paths and predict the future propagation range of the target event. Extensive experiments demonstrate effectiveness of NT-GP.
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References
Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 199–208. ACM (2009)
Liu, L., Li, X., Cheung, W.K., et al.: A structural representation learning for multi-relational networks. In: Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 4047–4053 (2017)
Ribeiro, L.F.R., Saverese, P.H.P., Figueiredo, D.R.: struc2vec: learning node representations from structural identity, pp. 385–394 (2017)
Bourigault, S., Lamprier, S., Gallinari, P.: Representation learning for information diffusion through social networks: an embedded cascade model. In: ACM International Conference on Web Search and Data Mining, pp. 573–582. ACM (2016)
Jenders, M., Kasneci, G., Naumann, F.: Analyzing and predicting viral tweets, pp. 657–664. ACM (2013)
Weng, L., Menczer, F., Ahn, Y.Y.: Predicting successful memes using network and community structure. Eprint Arxiv (2014)
Sitnikov, K.: Learning diffusion probability based on node attributes in social networks. In: Proceedings of the Foundations of Intelligent Systems - International Symposium, ISMIS 2011, Warsaw, Poland, 28–30 June 2011, pp. 153–162. DBLP (2011)
Zhang, J., Liu, B., Tang, J., et al.: Social influence locality for modeling retweeting behaviors. In: International Joint Conference on Artificial Intelligence, pp. 2761–2767. AAAI Press (2013)
Mikolov, T., Corrado, G., Chen, K., et al.: Efficient estimation of word representations in vector space. In: International Conference on Learning Representations, pp. 1–12 (2013)
Mikolov, T., Sutskever, I., Chen, K., et al.: Distributed representations of words and phrases and their compositionality. In: International Conference on Neural Information Processing Systems, pp. 3111–3119. Curran Associates Inc. (2013)
Li, D., Luo, Z., Ding, Y., et al.: User-level microblogging recommendation incorporating social influence. J. Assoc. Inf. Sci. Technol. 68(3), 553–568 (2017)
Xie, X., Li, Y., Zhang, Z., et al.: A joint link prediction method for social network. IFIP Adv. Inf. Commun. Technol. 503, 56–64 (2015)
Haghani, S., Keyvanpour, M.R.: A systemic analysis of link prediction in social network. Artif. Intell. Rev. 3, 1–35 (2017)
Li, C., Ma, J., Guo, X., et al.: DeepCas: an end-to-end predictor of information cascades. In: International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, pp. 577–586 (2017)
Acknowledgment
This work was supported by the National Natural Science Foundation of China (No. 61602159), the Natural Science Foundation of Heilongjiang Province (No. F201430), the Innovation Talents Project of Science and Technology Bureau of Harbin (No. 2017RAQXJ094, No. 2017RAQXJ131), and the fundamental research funds of universities in Heilongjiang Province, special fund of Heilongjiang University (No. HDJCCX-201608).
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Liu, Z., Wang, R., Liu, Y. (2019). Prediction Model for Non-topological Event Propagation in Social Networks. In: Cheng, X., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1058. Springer, Singapore. https://doi.org/10.1007/978-981-15-0118-0_19
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DOI: https://doi.org/10.1007/978-981-15-0118-0_19
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