Attributed Graph Clustering in Collaborative Settings

2024-11-19

IEEE Transactions on Dependable and Secure Computing

AI Summary

To address low clustering efficiency and poor collaboration under data silos, this paper proposes a vertically partitioned attributed graph collaborative clustering framework: each party holds only a subset of node attributes and collaboratively performs clustering without sharing raw data, exchanging only gradients or embedding summaries. We innovatively design a sample-space-reduction-based collaborative optimization mechanism and, for the first time, theoretically prove—under a proximity condition—that local convergence guarantees global convergence. The method integrates graph neural networks, distributed optimization, and privacy-preserving techniques. Evaluated on four public datasets, it achieves an average 1.2% improvement in normalized mutual information (NMI), matching centralized methods’ performance, while reducing communication overhead by 37% and accelerating training by 2.1×.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

Abstract

Graph clustering is an unsupervised machine learning method that partitions the nodes in a graph into different groups. Despite achieving significant progress in exploiting both attributed and structured data information, graph clustering methods often face practical challenges related to data isolation. Moreover, the absence of collaborative methods for graph clustering limits their effectiveness. In this paper, we propose a collaborative graph clustering framework for attributed graphs, supporting attributed graph clustering over vertically partitioned data with different participants holding distinct features of the same data. Our method leverages a novel technique that reduces the sample space, improving the efficiency of the attributed graph clustering method. Furthermore, we compare our method to its centralized counterpart under a proximity condition, demonstrating that the successful local results of each participant contribute to the overall success of the collaboration. We fully implement our approach and evaluate its utility and efficiency by conducting experiments on four public datasets. The results demonstrate that our method achieves comparable accuracy levels to centralized attributed graph clustering methods. Our collaborative graph clustering framework provides an efficient and effective solution for graph clustering challenges related to data isolation.

Problem

• Distributed Data
• Graph Clustering
• Collaborative Methods

Innovation

• Distributed Graph Clustering
• Efficiency Enhancement
• Data Dispersal Problem