Scalable and Adaptive Large-Scale Group Decision Making in Dynamic Social Networks via Graph Convolutional Neural Networks#

• GCD-GNN enables adaptive large-scale consensus in dynamic, trust-aware social networks. • Models trust dynamics, preference similarity, and evolving structures to stabilize consensus formation. • Validated on synthetic and real-world datasets, ensuring robustness and adaptability. • Bridges graph neural networks with large-scale group decision-making theory for intelligent decision support. As social networks are constantly changing, decision-making in large groups becomes much more challenging. People form new connections, lose old ones, shift their preferences, and change how much they trust others ( Qin, Li, Liang and Pedrycz, 2026 ). Methods that work well in stable settings often fail to keep pace here, especially when both quick adaptation and the ability to handle scale are essential ( Ding et al., 2025 ). Our approach, called GCD-GNN (Group Consensus Decision using Graph Neural Networks), builds on graph neural networks to track these ongoing changes in structure and preferences. It processes live updates on trust levels, social ties, and preference similarities, then adjusts influence weights in real time to keep the consensus process stable. In experiments using both synthetic and real-world datasets, GCD-GNN delivered higher agreement levels, improved accuracy and precision, and faster execution compared with leading alternatives. These results point to a framework that is not only scalable, but also able to adapt effectiveness in complex, large-scale decision-making environments.