超越学术单一文化：面向属性图聚类的统一框架与工业视角
Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering
AI总结：
本文提出统一框架和工业视角，分析属性图聚类在学术与工业需求间的差距，探讨评价标准、工程策略及未来研究方向。
摘要：
属性图聚类（AGC）是一种基础的无监督任务，通过联合建模结构拓扑和节点属性将节点划分为紧密群体。尽管图神经网络和自监督学习的出现推动了AGC方法的多样化，但学术基准性能与工业部署的严格需求之间仍存在鸿沟。本文从三个互补视角全面回顾AGC：首先，介绍Encode-Cluster-Optimize分类框架，将多样化算法分解为三个正交、可组合的模块：表示编码、聚类投影和优化策略。其次，批判性地检验现有评估协议，揭示领域学术单一文化：过度依赖小型、同质化的引用网络，监督-only指标对本质上无监督任务的不足，以及计算可扩展性的长期忽视。响应，倡导整合监督语义对齐、无监督结构完整性及严格效率分析的综合评估标准。第三，明确面对工业部署的实际挑战，通过分析大规模、严重异质性和表格特征噪声等操作约束，结合大量实证证据，提出可行的工程策略。此外，本文绘制了清晰的未来研究路线图，优先考虑异质性鲁棒编码器、可扩展的联合优化和无监督模型选择标准，以满足生产级需求。
英文摘要：
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 academic monoculture: a pervasive over-reliance on small, homophilous citation networks, the inadequacy of supervised-only metrics for an inherently unsupervised task, and the chronic neglect of computational scalability. In response, we advocate for a holistic evaluation standard that integrates supervised semantic alignment, unsupervised structural integrity, and rigorous efficiency profiling. Third, we explicitly confront the practical realities of industrial deployment. By analyzing operational constraints such as massive scale, severe heterophily, and tabular feature noise alongside extensive empirical evidence from our companion benchmark, we outline actionable engineering strategies. Furthermore, we chart a clear roadmap for future research, prioritizing heterophily-robust encoders, scalable joint optimization, and unsupervised model selection criteria to meet production-grade requirements.
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