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June 11, 2026MathematicsOpen Access

Modeling and Data Analysis of Innovation Dynamics in Complex Human–AI–Content Networks: A Multimodal Graph Learning Approach

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Authors

ZFZhou FangzhouLFLin FangHZHafizah Omar Zaki

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Overview

Randomized trial models innovation dynamics in human–AI–content networks, suggesting novel collaborative strategies.

Key Points

  • The study aims to model and analyze innovation dynamics in complex Human–AI–Content networks using a new graph learning approach.
  • Introduced a Multimodal Graph Neural Network (MM-GNN) for modeling tripartite graphs composed of human, AI, and content nodes.
  • Implemented attention-based fusion to integrate multimodal information like text and images.
  • Conducted experiments on synthetic HAC datasets and real-world AIGC corpora to assess model performance.
  • MM-GNN achieved an average F1 score of 0.87 and temporal stability ρ = 0.89 in modeling innovation dynamics.
  • Lower regression error compared to graph learning and index-based baselines was observed.
  • Ablation studies showed that multimodal fusion and temporal propagation improved representation quality and modeling accuracy.

Cite This Study

Fangzhou et al. (2026) studied this question.

synapsesocial.com/papers/6a2a508980c8f91e7f39cf91https://doi.org/10.3390/math14122051
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