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GRASS: Learning Spatial-Temporal Properties From Chainlike Cascade Data for Microscopic Diffusion Prediction.

Huacheng Li, Chunhe Xia, Tianbo Wang

    IEEE Transactions on Neural Networks and Learning Systems
    |July 19, 2023
    PubMed
    Summary
    This summary is machine-generated.

    The GRASS model enhances information diffusion prediction in social networks by addressing "position-hopping" and "branch-independency." It improves accuracy in predicting message spread dynamics.

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    Area of Science:

    • Social Network Analysis
    • Information Diffusion Dynamics
    • Machine Learning for Social Media

    Background:

    • Information diffusion prediction is crucial for tasks like viral marketing and popularity prediction.
    • Existing models struggle with cascade data due to lost spatial-temporal properties, leading to "position-hopping" and "branch-independency" issues.
    • These issues hinder accurate prediction of how information spreads through social networks.

    Purpose of the Study:

    • To propose a novel model, GRASS (GRU-like Attention Unit and Structural Spreading), for microscopic cascade prediction.
    • To overcome the limitations of "position-hopping" and "branch-independency" in current information diffusion models.
    • To enhance the accuracy and discriminative power of information diffusion prediction.

    Main Methods:

    • Integrating an attention mechanism into a gated recurrent unit (GRU) to expand the receptive field and address "position-hopping".
    • Implementing a structural spreading (SS) mechanism to filter relevant users and control cascade hidden state generation, tackling "branch-independency".
    • Utilizing t-distributed stochastic neighbor embedding (t-SNE) for visualizing latent representations and assessing model discriminative capabilities.

    Main Results:

    • The GRASS model significantly outperforms state-of-the-art baseline models on hits@κ and map@κ metrics.
    • Experimental results on real-world datasets demonstrate the effectiveness of GRASS in microscopic cascade prediction.
    • t-SNE visualizations confirm that GRASS generates more discriminative latent representations for different cascades.

    Conclusions:

    • The proposed GRASS model effectively addresses key challenges in information diffusion prediction.
    • GRASS offers improved accuracy and better representation of cascade dynamics compared to existing methods.
    • The model shows promise for advancing research in social network analysis and viral marketing applications.