Cascade Prediction model based on Dynamic Graph Representation and Self-Attention

被引：0
作者：
Zhang F. [1 ]
Wang X. [1 ]
Wang R. [1 ]
Tang Q. [1 ]
Han Y. [1 ]
机构：
[1] School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2022年 / 51卷 / 01期
关键词：
Cascades prediction; Deep learning; Dynamic graph representation; Information diffusion; Self-attention mechanism;
D O I：
10.12178/1001-0548.2021100
中图分类号：
学科分类号：
摘要：
The traditional cascade prediction models do not consider the dynamics features in the process of information diffusion and rely on artificial marking features heavily, which have the problems of poor generalization and low prediction accuracy. This paper proposes a cascade prediction model (information cascade with dynamic graphs representation and self-attention, DySatCas) that combines dynamic graph representation and self-attention mechanism. The model adopts an end-to-end approach, avoiding the difficult problem of cascade graph representation caused by artificial labeling features, capturing the dynamic evolution process of cascade graphs through sub-graph sampling. And it introduces a self-attention mechanism to better integrate in the dynamic structure changes and temporal characteristics of the information cascade graph learned in the observation window, which can allocate weight values to the network reasonably, reduce the loss of information, and improve the prediction performance. Experimental results show that DySatCas has significantly improved prediction accuracy compared with the existing baseline prediction model. Copyright ©2022 Journal of University of Electronic Science and Technology of China. All rights reserved.
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页码：83 / 90
页数：7
相关论文
共 19 条
  • [1] CHEN X, ZHOU F, ZHANG K, Et al., Information diffusion prediction via recurrent cascades convolution, 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 770-781, (2019)
  • [2] ZHANG Z, ZHANG F, TAN Q, Et al., review of information cascade prediction methods based on deep learning, Computer Science, 47, 7, pp. 141-153, (2020)
  • [3] ZHOU F, XU X, TRAJCEVSKI G, Et al., A survey of information cascade analysis: Models, predictions, and recent advances, ACM Computing Surveys (CSUR), 54, 2, pp. 1-36, (2021)
  • [4] ZHOU F, JING X, XU X, Et al., Continual information cascade learning, GLOBECOM 2020-2020 IEEE Global Communications Conference, pp. 1-6, (2020)
  • [5] ZHANG Q, GONG Y, WU J, Et al., Retweet prediction with attention-based deep neural network, Proceedings of the 25th ACM international on Conference on Information and Knowledge Management, pp. 75-84, (2016)
  • [6] GAO S, MA J, CHEN Z., Effective and effortless features for popularity prediction in microblogging network, Proceedings of the 23rd International Conference on World Wide Web, pp. 269-270, (2014)
  • [7] LEE J G, MOON S, SALAMATIAN K., Modeling and predicting the popularity of online contents with Cox proportional hazard regression model, Neurocomputing, 76, 1, pp. 134-145, (2012)
  • [8] SHEN H, WANG D, SONG C, Et al., Modeling and predicting popularity dynamics via reinforced poisson processes, Proceedings of the AAAI Conference on Artificial Intelligence, 28, 1, pp. 291-297, (2014)
  • [9] CAO Q, SHEN H, CEN K, Et al., Deephawkes: Bridging the gap between prediction and understanding of information cascades, Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1149-1158, (2017)
  • [10] FENG X, ZHAO Q, LIU Z., Prediction of information cascades via content and structure proximity preserved graph level embedding, Information Sciences, 560, pp. 424-440, (2021)
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