Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering
Attributed Graph Clustering (AGC) is a fundamental unsupervised task that partitions nodes into cohesive groups by jointly modeling structural topology and node attributes. While the advent of graph neural networks and self-supervised learning has catalyzed a proliferation of AGC methodologies, a widening chasm persists between academic benchmark performance and the stringent demands of real-world industrial deployment. To bridge this gap, this survey provides a comprehensive, industrially grounded review of AGC from three complementary perspectives. First, we introduce the Encode-Cluster-Optimize taxonomic framework, which decomposes the diverse algorithmic landscape into three orthogona...
| Authors | Yunhui Liu, Yue Liu, Yongchao Liu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 March 2026 |
| Last updated at source | 21 March 2026 |
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