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SkipCas: Information Diffusion Prediction Model Based on Skip-Gram

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Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13714))

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Abstract

The development of social network platforms such as Twitter and Weibo has accelerated the generation and transmission of information. Predicting the growth size of the information cascade is widely used in the fields of preventing rumor spread, viral marketing, recommendation system and so on. However, most of the existing methods either cannot fully capture the structural representation of the cascade graph, or cannot effectively utilize the dynamic changes of information diffusion, which often leads to poor prediction results. Therefore, in this paper, we propose a novel deep learning model called SkipCas to predict the growth size of the information cascade. First, we use the diffusion path and time effect at each diffusion time in the cascade graph to obtain the dynamic process of the information diffusion. Second, we put the sequence of biased random walk sampling into the skip-gram model to obtain the structural representation of the cascade graph. Finally, we combine the dynamic diffusion process and the structural representation to predict the growth size of the information cascade. Extensive experiments on two real datasets show that our model SkipCas significantly improves the prediction accuracy compared with the state-of-the-art models.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61972135), the Natural Science Foundation of Heilongjiang Province in China (No. LH2020F043), and the Foundation of Graduate Innovative Research of Heilongjiang University in China (No. YJSCX2022-236HLJU).

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Authors and Affiliations

  1. School of Computer Science and Technology, Heilongjiang University, Harbin, China

    Dedong Ren & Yong Liu

Authors
  1. Dedong Ren
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  2. Yong Liu
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Corresponding author

Correspondence to Yong Liu .

Editor information

Editors and Affiliations

  1. Grenoble Alpes University, Saint Martin d'Hères, France

    Massih-Reza Amini

  2. INSA Rouen Normandy, Saint Etienne du Rouvray, France

    Stéphane Canu

  3. Ruhr-Universität Bochum, Bochum, Germany

    Asja Fischer

  4. KU Leuven, Leuven, Belgium

    Tias Guns

  5. Central European University, Vienna, Austria

    Petra Kralj Novak

  6. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Grigorios Tsoumakas

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Ren, D., Liu, Y. (2023). SkipCas: Information Diffusion Prediction Model Based on Skip-Gram. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13714. Springer, Cham. https://doi.org/10.1007/978-3-031-26390-3_16

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  • DOI: https://doi.org/10.1007/978-3-031-26390-3_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26389-7

  • Online ISBN: 978-3-031-26390-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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