Adaptive-Probabilistic-Hierarchical-Search-APHS-

Adaptive Probabilistic Hierarchical Search (APHS): A Novel Framework for Dynamic Heterogeneous Graph Optimization Adaptive Probabilistic Hierarchical Search (APHS): A Novel Framework for Dynamic Heterogeneous Graph Optimization Abstract Dynamic heterogeneous graphs are ubiquitous in real-world applications, from logistics and fraud detection to social network analysis. However, existing graph algorithms often struggle to balance efficiency, adaptivity, and scalability in such complex environments. This paper introduces Adaptive Probabilistic Hierarchical Search (APHS), a novel framework that combines dynamic programming principles, graph neural networks (GNNs), and reinforcement learning (RL) to optimize graph-based tasks. APHS dynamically decomposes graphs into context-aware subgraphs, employs RL-guided prioritization for efficient exploration, and integrates time-decay functions to handle temporal dynamics. We evaluate APHS on benchmark datasets and real-world applications, demonstrating superior performance in tasks like route optimization and fraud detection compared to state-of-the-art methods like HiBO and AHLCSA. Our results highlight APHS’s potential to revolutionize graph-based optimization in dynamic, heterogeneous environments.