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July 29, 2026Discover Artificial IntelligenceOpen Access

Prediction of viral spread of digital content based on dynamic engagement graph and graph neural networks

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Authors

WZWei ZhouYunnan University

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Overview

Randomized trial predicts viral spread of digital content, highlighting improved accuracy and adaptability.

Key Points

  • The research aims to develop a framework for predicting the viral spread of digital content leveraging dynamic engagement graphs and graph neural networks.
  • Proposed a framework using dynamic engagement graphs and spatio-temporal graph neural networks.
  • Integrated neighbor features through graph convolutional structures and modeled spread processes with gated recurrent mechanisms.
  • Utilized time-decay weighting and semantic fusion to emphasize recent and significant interactions.
  • Achieved a stable root mean square error of around 0.20 after 200 iterations, a reduction of 16–28% compared to existing methods.
  • Obtained a top-twenty hit rate of 0.86, improving by 11–19% over other methods, and enhanced rank correlation coefficient by over 18%.
  • Demonstrated higher prediction accuracy, faster convergence speed, and better cross-platform adaptability.

Cite This Study

Wei Zhou (2026) studied this question.

synapsesocial.com/papers/6a69a26dc8da07d9defa5c34https://doi.org/10.1007/s44163-026-01838-4
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