MO-DMLHM: Multi-objective dynamic hypergraph modeling for cross-layer community detection in organizational networks

Abstract

Community detection in organizational networks is vital for optimizing team structures, yet existing methods face critical challenges: Static models ignore temporal dynamics, dynamic single-layer approaches overlook cross-layer interactions, and multi-objective frameworks often optimize goals in isolation, leading to suboptimal real-world performance. We propose the Multi-Objective Dynamic Multi-Layer Hypergraph Modeling Framework (MO-DMLHM), integrating three innovations: (1) Adaptive Dynamic Hypergraph Modeling with dual-scale decay and adaptive time windowing to capture spatiotemporal dynamics; (2) Four-Dimensional Multi-Objective Optimization balancing modularity, cross-layer consistency, stability, and efficiency via Pareto-optimal NSGA-III; (3) Hybrid Encoding Evolutionary Algorithm jointly optimizing hyperedge activation and node membership through spectral clustering-guided mutation and betweenness centrality-driven crossover. Experiments on diverse organizational networks show MO-DMLHM outperforms state-of-the-art methods in detection accuracy, cross-layer alignment, and stability, reducing coordination costs by nearly 40%. Ablation studies confirm the necessity of dynamic modeling, multi-objective optimization, and hybrid encoding. MO-DMLHM resolves structural-community decoupling in dynamic multi-layer systems, advancing complex network analysis and enabling adaptive governance in organizations, with extensions to smart cities, biological networks, and financial risk management.