Deep graph clustering with multi-level subspace fusion

Abstract

Attributed graph clustering combines both node attributes and graph structure information of data samples and has demonstrated satisfactory performance in various applications. However, how to choose the proper neighborhood for attributed graph clustering remains to be a challenge. A larger neighborhood may cause over-smoothed representations with less discrimination for clustering while the short-range ignore distant nodes and fails to capture the global information. In this paper, we propose a novel deep attributed graph clustering network with a multi-level subspace fusion module to address this issue. The first contribution of our work is to insert multiple self-expressive modules between low-level and high-level layers to promote more favorable features for clustering. The constraint of shared self-expressive matrix facilitates to preserve intrinsic structure without pre-defined neighborhoods as the previous methods do. Moreover, we introduce a novel loss function that leverages traditional reconstruction and the proposed structure fusion loss to effectively preserve multi-level clustering structures with both global and local discriminative features. Extensive experiments on public benchmark datasets validate the effectiveness of our proposed model compared with the state-of-the-art attribute graph clustering competitors by considerable margins.