A machine learning framework for structural and predictive analysis of intelligent data networks.

Résumé

The exponential growth of Intelligent Data Networks (IDNs) complex, dynamic systems encompassing IoT ecosystems, social networks, and cyber physical infrastructures-has exposed significant limitations in current analytical methodologies. Traditional approaches remain fundamentally siloed, treating structural analysis and predictive forecasting as distinct challenges addressed by specialized models. This artificial separation restricts holistic understanding and impedes the extraction of synergistic insights from network data. This paper introduces NETSTRUCTPRED, a novel, unified machine learning framework explicitly designed for the joint structural and predictive analysis of heterogeneous, temporal IDNs. The framework's architecture integrates a meta-path guided heterogeneous graph encoder, a continuous-time memory-augmented transformer for temporal dynamics, and a multi-task learning core with a novel cross-task feedback mechanism. This design fosters the learning of unified representations that simultaneously encode topological properties, semantic relationships, and evolutionary patterns. Through comprehensive experimentation across six diverse real-world datasets-spanning academic (DBLP, Aminer), social (Wikipedia, Reddit), transportation (METR-LA), and cybersecurity (IoT-23) domains-we demonstrate that NETSTRUCTPRED achieves state-of-the-art or competitive performance in both structural and predictive tasks. The framework outperformed twelve specialized baseline models, attaining a 0.742 Normalized Mutual Information score for community detection on DBLP while maintaining a 0.924 AUC-ROC for link prediction, and achieving a traffic forecasting MAE of 2.84 mph on METR-LA while simultaneously identifying congestion-correlated sensor communities. The integrated approach yielded a superior Structural-Predictive Efficiency score of 0.873 on average, confirming balanced excellence across analytical dimensions. Critical case studies underscore the framework's practical utility: in cybersecurity, it identified attack-susceptible device clusters 45 minutes before behavioral anomalies manifested; in traffic management, it revealed structural communities that enabled a 23% improvement in incident clearance through informed routing. Ablation studies validated the necessity of each architectural component and the cross-task feedback loop, while scalability analysis confirmed near-linear computational scaling to networks with millions of nodes. The framework also demonstrated enhanced robustness to noise and superior transfer learning capabilities, achieving 89% of fully supervised performance when fine-tuned with minimal target data. These results collectively validate that the integration of structural and predictive analytics is not merely complementary but fundamentally synergistic, providing richer insights, more accurate forecasts, and actionable intelligence for the management and optimization of next-generation intelligent networks.