Attributed Graph Clustering in Collaborative Settings

TL;DR

This paper introduces a collaborative framework for attributed graph clustering that enables multiple participants with different data features to jointly cluster nodes efficiently, overcoming data isolation issues.

Contribution

It proposes a novel collaborative graph clustering method for vertically partitioned data, improving efficiency and maintaining accuracy comparable to centralized approaches.

Findings

Achieves clustering accuracy similar to centralized methods.

Improves efficiency through a novel sample space reduction technique.

Effectively handles data isolation in collaborative settings.

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.