Reinforcement Learning-Enhanced Graph Neural Networks for Multi-Scale, Richly Labeled Networks in Interconnected Systems

The study of interconnected, multi-scale networks is essential in uncovering complex relationships across diverse domains, such as biomedical research, information security, and social sciences. This paper introduces a novel framework that integrates Reinforcement Learning (RL) with Graph Neural Net-works (GNNs) for analyzing multi-scale, richly labeled networks. Most traditional GNN architectures cannot effectively represent heterogeneous data that are in continuous evolution at multiple scales of interaction. Our framework overcomes these limitations by leveraging an RL agent to optimize multi-scale attention and label encoding with regard to real-time network dynamics, hence ensuring better adaptability and scalability of the model across domains like gene-protein interaction networks, tracking influence in social media, and detection of advanced persistent threats. The results underscore the efficacy of combining RL and GNNs for improved cross-domain analysis, contributing significantly to the field of complex network modeling.