Towards network subspace clustering using low-rank tensor singular value decomposition and graph-boosting

Network subspace clustering aims to uncover meaningful clusters of nodes by identifying low-dimensional subspaces embedded in complex and large-scale network data. Recent approaches have leveraged singular value decomposition and graph-boosting mechanisms to improve clustering performance; however, they often suffer from high computational complexity, sensitivity to noise, and the inability to capture nonlinear structural patterns. Moreover, existing methods fail to recover both the global low-rank structure of graphs and the concealed high-order relationships among nodes. To address these challenges, this paper proposes an efficient Network subspace Clustering framework using Low-rank Tensor singular value decomposition and Graph-Boosting (NCLTGB). Specifically, the tensor singular value decomposition module maps large-scale network data via a feature transition matrix into a clean low-rank feature space, effectively separating structural information from noise and retrieving high-quality latent features. After denoising and outlier elimination, a graph-boosting module—comprising an auxiliary graph and an enhanced graph—is employed to enforce long-range node relationships and strengthen global structural representations. Furthermore, NCLTGB incorporates a dual-guided pseudo-Siamese neural network to train the model, and the final subspace clustering is derived from the learned representations. Extensive experiments on multiple real-world datasets demonstrate that NCLTGB consistently outperforms advanced approaches, including MFK and SDAC-DA, in terms of both clustering accuracy and robustness.