Stability-Regularized Temporal Graph Learning for Dynamic Community Detection in Evolving Networks
Keywords:
Temporal Graph Learning, Community Detection, Evolving Networks, Stability Regularization, Graph Neural NetworksAbstract
The study of evolving networks has become increasingly crucial for understanding complex systems ranging from social media interactions to biological neural pathways. While standard community detection algorithms excel in static environments, they frequently struggle with dynamic graphs, often producing highly unstable community partitions that fluctuate erratically due to minor structural perturbations. This paper introduces a comprehensive framework for stability-regularized temporal graph learning, specifically designed to mitigate the volatility inherent in dynamic community detection. By integrating a temporal regularization term directly into the optimization landscape of a graph neural network, the proposed architecture enforces temporal smoothness while retaining sufficient plasticity to adapt to genuine structural evolutions. The methodology involves processing continuous network snapshots through a specialized temporal attention mechanism, balanced by a novel stability loss function that penalizes unwarranted deviations in cluster assignments across consecutive time steps. Extensive empirical evaluations are conducted across multiple distinct datasets, representing communication networks, citation graphs, and transactional environments. The results demonstrate that the stability-regularized approach significantly outperforms existing state-of-the-art dynamic graph learning models in both clustering accuracy and temporal consistency metrics. The framework offers a robust theoretical and practical foundation for analyzing highly volatile network topologies, providing deeper insights into the underlying mechanisms of structural evolution in complex domains.
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