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

In complex socio-technical systems, human–AI collaboration is becoming fundamental to the processes of knowledge creation, content generation, and innovation. The existing innovation models typically consider only a single actor, the sole AI system, or a content artifact, and therefore do not capture the dynamics between these heterogeneous actors. This study introduces a Multimodal Graph Neural Network (MM-GNN), for modeling and analyzing innovation dynamics within Human–AI–Content (HAC) networks. The proposed framework is based on HAC networks as dynamic tripartite graphs, where human nodes, AI agent nodes, and content nodes are interconnected by edges representing interactions that evolve over time. Multimodal information, including text, image, code, and structured interaction traces, is merged by attention-based fusion, and multimodal dependency and evolution of interactions are modeled by relation-aware graph message passing and GRU-based temporal propagation. The innovation potential is realized as an upper-bounded composite score based on normalized novelty, entropy change, diffusion contribution, and human-rated creativity if available. The model is assessed as a composition of node-level classification and a regression model for innovation-level classification and estimation of continuous innovation potential. Experiments on synthetic HAC datasets and selected real-world AIGC corpora demonstrate that MM-GNN performs better than the graph learning and index-based baselines, with an average F1 score of 0.87, temporal stability ρ = 0.89, and lower regression error. The ablation and visualization analyses demonstrate that the multimodal fusion and temporal propagation are beneficial for representation quality, diffusion modeling, and interpretation. The results offer a mathematical and computational approach to the study of innovation as an emergent phenomenon of dynamic human, AI, and content interactions and lay the groundwork for additional validation on a more expansive socio-technical scale.