Revisiting Dynamic Graph Clustering via Matrix Factorization

TL;DR

This paper introduces a scalable, robust, and efficient matrix factorization approach for dynamic graph clustering, capable of handling large-scale, noisy, and evolving graph data effectively.

Contribution

It proposes temporal separated matrix factorization, bi-clustering regularization, and selective embedding updating to improve dynamic graph clustering.

Findings

Demonstrates improved scalability on large graphs.

Shows enhanced robustness against noisy data.

Achieves better clustering accuracy and efficiency.

Abstract

Dynamic graph clustering aims to detect and track time-varying clusters in dynamic graphs, revealing the evolutionary mechanisms of complex real-world dynamic systems. Matrix factorization-based methods are promising approaches for this task; however, these methods often struggle with scalability and can be time-consuming when applied to large-scale dynamic graphs. Moreover, they tend to lack robustness and are vulnerable to real-world noisy data. To address these issues, we make three key contributions. First, to improve scalability, we propose temporal separated matrix factorization, where a single matrix is divided into multiple smaller matrices for independent factorization, resulting in faster computation. Second, to improve robustness, we introduce bi-clustering regularization, which jointly optimizes graph embedding and clustering, thereby filtering out noisy features from the graph embeddings. Third, to further enhance effectiveness and efficiency, we propose selective embedding updating, where we update only the embeddings of dynamic nodes while the embeddings of static nodes are fixed among different timestamps. Experimental results on six synthetic and five real-world benchmarks demonstrate the scalability, robustness and effectiveness of our proposed method. Source code is available at https://github.com/Clearloveyuan/DyG-MF.