Aug 2026· International Journal of Data Science and Analysis· Vol 22· 0 citations· 42 references
TL;DR
This paper introduces a joint learning framework for updating attributes to be smoother via the deep structure of the graph which is obtained by high-order proximities and updates the attributes so that their induced geometrical structure is similar to the graph structure.
Attributed graph clustering aims to discover node groups by jointly exploiting node attributes and graph topology, yet its unsupervised nature makes model selection and adaptation inherently difficult. Existing methods typically train and tune a separate model for each input graph, leading to costly and fragile pipelin...
Yunhui Liu, Xu-Dong Jin, Kang Zhang et al.· 1 citation
This work uses Graph Neural Networks (GNNs) to solve Inductive Correlation Clustering, a novel generalization of the CC problem designed to handle unseen graph instances, and indicates that the method serves as an efficient pooling layer, enhancing the ability of GNNs to capture hierarchical structural information in n...
Francesco Paolo Nerini, Francesco Bonchi, Arijit Khan et al.· 0 citations
This paper proposes a feature-graph-guided adaptive Log-L2,1 sparse NMF with anchor dual graphs under a logarithmic framework that jointly integrate sample structure preservation, feature structure preservation, and feature-aware sparse learning within a unified graph-NMF model.
Quanrun Li, Tao Ma, Fangchen Xu et al.· Mathematics· 0 citations
Current Multiview Graph Clustering (MGC) methods primarily focus on fusing multiple graphs and/or spectral embeddings to seek common clustering sets, however, the tendency between nodes and the locality between clusters and views are scarcely considered. Specifically, there are consistent topological structures in mult...
Danyang Wu, Peng-Lei Wang, Jun-Jie Liang et al.· IEEE Transactions on Pattern...· 0 citations
The Dual-tOpology learning with adapTive Anchors (DOTA) is proposed, which not only learns the sample-anchor relationship but also preserves the topology structure among anchors, significantly enhancing the discriminability of learned representation while preserving the underlying data manifold.
Cheng-Long Zhang, Chao Zhang, Jun-Hao Zhang et al.· Proceedings of the Thirty-Fi...· 0 citations
ELSS learns an explicit and nonlinear low-rank subspace within a graph-structured embedding space, effectively un-covering latent cluster structures and introduces a homophily-aware adaptive graph filter, which dynamically calibrates smoothing intensity to preserve discriminative ego-information.
Yao-Ming Cai, Song Liu, Zijia Zhang et al.· Proceedings of the Thirty-Fi...· 0 citations
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