Open Access
Issue
MATEC Web Conf.
Volume 336, 2021
2020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
Article Number 05012
Number of page(s) 8
Section Computer Science and System Design
DOI https://doi.org/10.1051/matecconf/202133605012
Published online 15 February 2021
  1. D. Gruhl, R. Guha, D. Liben-Nowell, A. Tomkins. Information diffusion through blogspace. Proceedings of the 13th international conference on World Wide Web. 491-501(2004). [Google Scholar]
  2. Golub, Benjamin, Jackson, O. Matthew, Using selection bias to explain the observed structure of internet diffusions. Proceedings of the National Academy of Sciences.107(24): 10833-10836(2010). [Google Scholar]
  3. J. Qiu, J. Tang, H. Ma, Deepinf: Social influence prediction with deep learning. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2110-2119(2018). [Google Scholar]
  4. Z. Ma, A. Sun, G. Cong. On predicting the popularity of newly emerging hashtags in T witter. Journal of the American Society for Information Science and Technology.64(7): 1399-1410(2013). [Google Scholar]
  5. G. Szabo, B.A. Huberman, Predicting the popularity of online content. Communications of the ACM.53(8): 80-88(2010). [Google Scholar]
  6. Z.Y. Zhang, F.Z. Zhang, Q. Tan, et al. Review of Information Cascade Prediction Methods Based on Deep Learning. Computer science, 47(7): 141-153. (in Chinese) [Google Scholar]
  7. L. Weng, F. Menczer, Y.Y. Ahn. Virality prediction and community structure in social networks. Scientific reports. 3: 2522(2013). [Google Scholar]
  8. Q. Zhao, M.A. Erdogdu, H.Y. He, et al. Seismic: A self-exciting point process model for predicting tweet popularity. Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. 1513-1522(2015). [Google Scholar]
  9. M. Gomez-Rodriguez, J. Leskovec, B. Schölkopf. Modeling information propagation with survival theory. International Conference on Machine Learning. 666-674(2013). [Google Scholar]
  10. E. Manavoglu, D. Pavlov,L.C. Giles, Probabilistic user behavior models. Third IEEE International Conference on Data Mining. IEEE.203-210(2003). [CrossRef] [Google Scholar]
  11. T. Mikolov, S. Kombrink, L. Burget, et al. Extensions of recurrent neural network language model. 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 5528-5531(2011). [Google Scholar]
  12. Y. Wang, H. Shen, S. Liu, et al. Cascade Dynamics Modeling with Attention-based Recurrent Neural Network. IJCAI. 2985-2991(2017). [Google Scholar]
  13. H. Chen, J. Liu, Y. Lv, et al. Semi-supervised clue fusion for spammer detection in Sina Weibo. Information Fusion.44: 22-32(2018). [Google Scholar]
  14. Z. Wang, W. Li. Hierarchical Diffusion Attention Network. IJCAI.3828-3834(2019). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.