Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
A Hybrid Representation of Word Images for Keyword Spotting Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Predicting Information Diffusion Cascades Using Graph Attention Networks

Authors : Meng Wang, Kan Li

Published in: Neural Information Processing

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Online Multi-objective Subspace Clustering for Streaming Data
next chapter PrivRec: User-Centric Differentially Private Collaborative Filtering Using LSH and KD
Literature
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)
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)
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)
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)
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)
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)
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)
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)
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)
10.
Szabo, G., Huberman, B.A.: Predicting the popularity of online content. Commun. ACM 53(8), 80–88 (2010)CrossRef
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)
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)
Metadata
Title
Predicting Information Diffusion Cascades Using Graph Attention Networks
Authors
Meng Wang
Kan Li
Copyright Year
2020
Book
Neural Information Processing

Print ISBN: 978-3-030-63819-1

Electronic ISBN: 978-3-030-63820-7

Copyright Year: 2020

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

Premium Partner

    Image Credits