Beyond Implicit Constraint: Explicit Low-Rank Structured Subspace Learning for Fast Attributed Graph Clustering

Dual-Topology Learning with Adaptive Anchors for Multi-View Clustering

Unsupervised Graph Representation Learning with Complementary View Alignment

This framework introduces a dual-encoder architecture that separately processes structural and attribute information, incorporates node positional encoding to approximate Neighborhood Identity Distribution (NID), and employs dual reconstruction tasks for both edges and node attributes.

Feature-Graph-Guided Adaptive Sparse NMF with Anchor Dual Graphs Under the Logarithmic Framework for Data Clustering

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.

D2GSL: Self-Supervised Dual-Layer Structure-Driven Graph Structure Learning

D2GSL constructs a semantic similarity channel and a spectral feature channel to model node relationships from both local semantic and global spectral views and introduces a hyperadjacency matrix that explicitly models inter-layer node correspondences and enables joint structural reconstruction across channels.

One-for-All Community Search on Unseen Graphs

A Spectral-Aware Feature Alignment module to unify feature dimensionality and align cross-domain semantics in a community-aware manner and a Graph Diffusion Tokenized Transformer that constructs hybrid token sequences from local and global structural contexts for Transformer encoding, and applies diffusion-based refinement to mitigate distribution shifts on unseen graphs.

DiffGCC: diffusion-enhanced global–local graph contrastive clustering

DiffGCC is a generative graph contrastive clustering framework that couples global–local feature encoding with a latent-space diffusion denoising mechanism and substantially outperforms existing methods across ACC, NMI, ARI, and F1, with particularly strong gains on denser, noisier product graphs.