[2603.20829] Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering

Computer Science > Machine Learning arXiv:2603.20829 (cs) [Submitted on 21 Mar 2026] Title:Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering Authors:Yunhui Liu, Yue Liu, Yongchao Liu, Tao Zheng, Stan Z. Li, Xinwang Liu, Tieke He View a PDF of the paper titled Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering, by Yunhui Liu and 6 other authors View PDF HTML (experimental) Abstract: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 orthogonal, composable modules: representation encoding, cluster projection, and optimization strategy. This unified paradigm enables principled architectural comparisons and inspires novel methodological combinations. Second, we critically examine prevailing evaluation protocols to expose the field's...