Temporal Graph Intelligence: a Continual Learning Framework for Evolving Network Structures, Event-driven Anomaly Detection, and Decentralized Knowledge Fusion Developing Self-adaptive Network Science
Year: 2025
Type: article
Abstract: The dynamic nature of complex data ecosystems presents significant challenges for conventional graph learning methods. This paper introduces Temporal Graph Intelligence (TGI), a novel framework that leverages Spatio-Temporal Graph Transformers (STGT), continual learning paradigms, and multiscale pattern discovery for robust analysis of evolving network structures. TGI addresses three key challenges: (1) evolving network topologies, (2) real-time anomaly detection in event streams, and (3) decentralized learning in distributed environments. Our approach facilitates self-adaptive network science by integrating spatio-temporal context and hierarchical temporal dependencies. We evaluate TGI on synthetic and real-world datasets, demonstrating significant improvements over baseline models in anomaly detection accuracy, temporal reasoning, and resilience to concept drift.
Author: Researcher
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893296065
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