Federated Temporal Graph Clustering

TL;DR

This paper presents a federated learning framework for temporal graph clustering that preserves data privacy, captures dynamic graph evolution, and achieves competitive results without centralized data collection.

Contribution

It introduces a novel Federated Temporal Graph Clustering (FTGC) framework combining temporal aggregation and federated optimization for privacy-preserving dynamic graph analysis.

Findings

Achieves competitive clustering performance on temporal graph datasets.

Reduces communication overhead compared to centralized methods.

Ensures data privacy across multiple clients.

Abstract

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.