Skip to main content
SpringerLink
Log in
Book cover

International Conference on Neural Information Processing

ICONIP 2020: Neural Information Processing pp 104–112Cite as

Predicting Information Diffusion Cascades Using Graph Attention Networks

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1332))

Abstract

Effective information cascade prediction plays a very important role in suppressing the spread of rumors in social networks and providing accurate social recommendations on social platforms. This paper improves existing models and proposes an end-to-end deep learning method called CasGAT. The method of graph attention network is designed to optimize the processing of large networks. After that, we only need to pay attention to the characteristics of neighbor nodes. Our approach greatly reduces the processing complexity of the model. We use realistic datasets to demonstrate the effectiveness of the model and compare the improved model with three baselines. Extensive results demonstrate that our model outperformed the three baselines in the prediction accuracy.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bao, P., Shen, H.W., Huang, J., Cheng, X.Q.: Popularity prediction in microblogging network: a case study on Sina Weibo. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 177–178 (2013)

    Google Scholar 

  2. 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)

    Google Scholar 

  3. 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)

    Google Scholar 

  4. Cui, P., Jin, S., Yu, L., Wang, F., Zhu, W., Yang, S.: Cascading outbreak prediction in networks: a data-driven approach. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 901–909 (2013)

    Google Scholar 

  5. Hong, L., Dan, O., Davison, B.D.: Predicting popular messages in Twitter. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 57–58 (2011)

    Google Scholar 

  6. Lerman, K., Galstyan, A.: Analysis of social voting patterns on digg. In: Proceedings of the First Workshop on Online Social Networks, pp. 7–12 (2008)

    Google Scholar 

  7. 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)

    Google Scholar 

  8. Pinto, H., Almeida, J.M., Gonçalves, M.A.: Using early view patterns to predict the popularity of YouTube videos. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 365–374 (2013)

    Google Scholar 

  9. Shen, H., Wang, D., Song, C., Barabási, A.L.: Modeling and predicting popularity dynamics via reinforced Poisson processes. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)

    Google Scholar 

  10. Szabo, G., Huberman, B.A.: Predicting the popularity of online content. Commun. ACM 53(8), 80–88 (2010)

    Article  Google Scholar 

  11. Tsur, O., Rappoport, A.: What’s in a hashtag? Content based prediction of the spread of ideas in microblogging communities. In: Proceedings of the fifth ACM International Conference on Web Search and Data Mining, pp. 643–652 (2012)

    Google Scholar 

  12. Weng, L., Menczer, F., Ahn, Y.Y.: Predicting successful memes using network and community structure. In: Eighth International AAAI Conference on Weblogs and Social Media (2014)

    Google Scholar 

Download references

Acknowledgments

This research was supported by Beijing Natural Science Foundation (No. L181010, 4172054), National Key R & D Program of China (No. 2016YFB0801100), and National Basic Research Program of China (No. 2013CB329605).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Meng Wang & Kan Li

Authors
  1. Meng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kan Li .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

  2. Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    James T. Kwok

  5. School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Irwin King

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, M., Li, K. (2020). Predicting Information Diffusion Cascades Using Graph Attention Networks. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-63820-7_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63819-1

  • Online ISBN: 978-3-030-63820-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation