Dynamic Topological Dependency Reasoning Network (DTTRN)

Abstract

This paper introduces the Dynamic Topological Dependency Reasoning Network (DTTRN), a novel neural architecture designed for real-time learning and adaptation of complex system topology dependencies. Traditional dependency analysis methods rely on static, pre-defined topologies, limiting their effectiveness in dynamic and evolving systems. DTTRN addresses this limitation by employing a multi-layered, variable topology graph neural network where nodes represent system components and edges represent dependencies. Reinforcement learning is utilized to dynamically adjust edge weights and topology structure based on system behavior, optimizing overall performance and robustness. Furthermore, knowledge graph embedding techniques are integrated to enrich node representations with domain knowledge, enhancing reasoning accuracy. The core claim of this work is to build an architecture capable of real-time learning and adaptation of complex system topology dependency relationships, surpassing the constraints of static dependency graphs. DTTRN achieves this through a synergistic combination of graph neural network adaptability, reinforcement learning optimization, and knowledge graph embedding representation, resulting in real-time, self-adaptive reasoning for complex systems, robust to behavioral changes and benefiting from contextual knowledge for improved precision and generalization.