A GCN-LSTM framework for link prediction in dynamic SIoT networks

Abstract

The Social Internet of Things (SIoT) paradigm combines the Internet of Things (IoT) with social networking principles, enabling autonomous device interactions. However, the dynamic, heterogeneous, and temporal nature of SIoT networks presents significant challenges for link prediction. While existing models, such as SIoTPredict (Aljubairy et al., 2020), exploits social network analysis and machine learning, they primarily focus only on certain aspects of social relationships between devices, lacking consideration of temporal dynamics, environmental factors, and device heterogeneity. In contrast, we propose a novel framework that integrates Graph Convolutional Networks (GCNs) with Long Short-Term Memory (LSTM) networks and a Multi-Head Attention mechanism, further enhanced by a dynamic feature scheduling strategy, to accurately capture the spatial and temporal dependencies in SIoT environments. Additionally, our work introduces a MATLAB-based SIoT Simulator, providing a robust platform for generating realistic dynamic scenarios and evaluating complex Social Object Relationships. Unlike SIoTPredict, which is optimized for real-time applications but focuses narrowly on social aspects, our approach incorporates a broader range of factors, enabling better predictive accuracy in scenarios with high temporal variability. Extensive co-simulations with MATLAB and PyTorch demonstrate that our framework significantly outperforms existing methods in terms of predictive accuracy, adaptability, and robustness. By bridging critical gaps in current methodologies, this work establishes a comprehensive foundation for future research and practical applications in dynamic SIoT networks.