A Graph Convolutional Network and Gated Recurrent Unit-based surrogate for agent-based diffusion models

This study addresses the challenge of high computational costs in agent-based diffusion models (ABMs), which are widely used for simulating complex diffusion processes but become prohibitively expensive in large-scale applications. To mitigate this issue, we introduce a Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU)-based Surrogate Network (G2SN) for ABMs. The GCN module captures the social network structure and seed set, while the GRU module models the diffusion time series. Computational complexity analysis demonstrates that G2SN significantly outperforms ABM simulations in efficiency. Experimental results confirm that G2SN accurately predicts ABM dynamics, reducing the mean absolute deviation (MAD) by 71.7 % on training sets and 77.7 % on test sets compared to traditional machine learning surrogate models. Case studies on new product diffusion further illustrate the effectiveness of the G2SN-based calibration approach, improving parameter search efficiency by 50.8 % and 37.2 % over alternative surrogate model-based methods. Additionally, these studies underscore the critical importance of social network and seed set in enhancing ABM prediction accuracy. This approach provides a more efficient and scalable tool for ABM calibration and new product diffusion forecasting, aiding managers in production, inventory, and marketing decisions.