Federated Temporal Graph Clustering
This work addresses privacy and communication challenges in dynamic graph analysis for real-world applications, representing an incremental improvement over centralized methods.
The paper tackles the problem of temporal graph clustering without centralized data by introducing a federated learning framework that preserves privacy and reduces communication overhead, achieving competitive performance on temporal graph datasets.
Temporal graph clustering is a complex task that involves discovering meaningful structures in dynamic graphs where relationships and entities change over time. Existing methods typically require centralized data collection, which poses significant privacy and communication challenges. In this work, we introduce a novel Federated Temporal Graph Clustering (FTGC) framework that enables decentralized training of graph neural networks (GNNs) across multiple clients, ensuring data privacy throughout the process. Our approach incorporates a temporal aggregation mechanism to effectively capture the evolution of graph structures over time and a federated optimization strategy to collaboratively learn high-quality clustering representations. By preserving data privacy and reducing communication overhead, our framework achieves competitive performance on temporal graph datasets, making it a promising solution for privacy-sensitive, real-world applications involving dynamic data.