Self-supervised deep geometric subspace clustering network

Graph mining has been widely studied to analyze real-world graph properties and applied to various applications. In particular, graph subspace clustering performance, defined as partitioning high-dimensional graph data into several clusters by finding minimum weights for the edges, has been consistently improved by exploiting deep learning algo-rithms with Euclidean features extracted from Euclidean domains (image datasets). Most subspace clustering algorithms tend to extract features from the Euclidean domain to iden-tify graph characteristics and structures, and hence are limited for real-world data applica-tions in non-Euclidean domains. This paper proposes a self-supervised deep geometric subspace clustering algorithm optimized for non-Euclidean high-dimensional graph data by emphasizing spatial features and geometric structures while simultaneously reducing redundant nodes and edges. Quantitative and qualitative experimental results verified the proposed approach is effective for graph clustering compared with previous state-of-the-art algorithms on public datasets.(c) 2022 Elsevier Inc. All rights reserved.