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Cascade-Enhanced Graph Convolutional Network for Information Diffusion Prediction

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Database Systems for Advanced Applications (DASFAA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13245))

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Abstract

Information diffusion prediction aims to estimate the probability of an inactive user to be activated next in an information diffusion cascade. Existing works predict future user activation either by capturing sequential dependencies within the cascade or leveraging rich graph connections among users. However, most of them perform prediction based on user correlations within the current cascade without fully exploiting diffusion properties from other cascades, which may contain beneficial collaborative patterns for the current cascade. In this paper, we propose a novel Cascade-Enhanced Graph Convolutional Networks (CE-GCN), effectively exploiting collaborative patterns over cascades to enhance the prediction of future infections in the target cascade. Specifically, we explicitly integrate cascades into diffusion process modeling via a heterogeneous graph. Then, the collaborative patterns are explicitly injected into unified user embedding by message passing. Besides, we design a cascade-specific aggregator to adaptively refine user embeddings by modeling different effects of collaborative features from other cascades with the guidance of user context and time context in the current cascade. Extensive experiments on three public datasets demonstrate the effectiveness of the proposed model.

D. Wang and L. Wei—Both are first authors with equal contributions.

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Notes

  1. 1.

    https://www.twitter.com.

  2. 2.

    https://www.douban.com.

  3. 3.

    http://memetracker.org/.

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Author information

Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Ding Wang, Lingwei Wei, Yinan Bao, Wei Zhou, Xian Zhu & Songlin Hu

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Ding Wang, Lingwei Wei, Yinan Bao, Xian Zhu & Songlin Hu

  3. JD.com, Beijing, China

    Chunyuan Yuan

Authors
  1. Ding Wang
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  2. Lingwei Wei
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  3. Chunyuan Yuan
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  4. Yinan Bao
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  5. Wei Zhou
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  6. Xian Zhu
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  7. Songlin Hu
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Corresponding author

Correspondence to Wei Zhou .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Kanpur, Kanpur, India

    Arnab Bhattacharya

  2. National University of Singapore, Singapore, Singapore

    Janice Lee Mong Li

  3. University of California, Santa Barbara, Santa Barbara, CA, USA

    Divyakant Agrawal

  4. IIIT Hyderabad, Hyderabad, India

    P. Krishna Reddy

  5. Indraprastha Institute of Information Technology Delhi, New Delhi, India

    Mukesh Mohania

  6. Ashoka University, Sonepat, Haryana, India

    Anirban Mondal

  7. Indraprastha Institute of Information Technology Delhi, New Delhi, India

    Vikram Goyal

  8. University of Aizu, Aizu, Japan

    Rage Uday Kiran

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Wang, D. et al. (2022). Cascade-Enhanced Graph Convolutional Network for Information Diffusion Prediction. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13245. Springer, Cham. https://doi.org/10.1007/978-3-031-00123-9_50

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  • DOI: https://doi.org/10.1007/978-3-031-00123-9_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-00122-2

  • Online ISBN: 978-3-031-00123-9

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