mlsanityData to 7 Oct 2026

Bridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clustering

arXiv 2602.08519 ↗First posted 9 Feb 2026cs.LGcited by 3

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Abstract (arXiv)

Attributed Graph Clustering (AGC) is a fundamental unsupervised task that integrates structural topology and node attributes to uncover latent patterns in graph-structured data. Despite its significance in industrial applications such as fraud detection and user segmentation, a significant chasm persists between academic research and real-world deployment. Current evaluation protocols suffer from the small-scale, high-homophily citation datasets, non-scalable full-batch training paradigms, and a reliance on supervised metrics that fail to reflect performance in label-scarce environments. To bridge these gaps, we present PyAGC, a comprehensive, production-ready benchmark and library designed to stress-test AGC methods across diverse scales and structural properties. We unify existing methodologies into a modular Encode-Cluster-Optimize framework and, for the first time, provide memory-efficient, mini-batch implementations for a wide array of state-of-the-art AGC algorithms. Our benchmark curates 12 diverse datasets, ranging from 2.7K to 111M nodes, specifically incorporating industrial graphs with complex tabular features and low homophily. Furthermore, we advocate for a holistic evaluation protocol that mandates unsupervised structural metrics and efficiency profiling alongside traditional supervised metrics. Battle-tested in high-stakes industrial workflows at Ant Group, this benchmark offers the community a robust, reproducible, and scalable platform to advance AGC research towards realistic deployment. The code and resources are publicly available via GitHub (https://github.com/Cloudy1225/PyAGC), PyPI (https://pypi.org/project/pyagc), and Documentation (https://pyagc.readthedocs.io).

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 26 of its 45 references that are in the map. First posted on arXiv 9 Feb 2026.

Builds on

26 of 45 references.

References · 26

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  6. 2016Variational Graph Auto-Encoderscited by 1,373
  7. 2018Deep Graph Infomaxcited by 1,101
  8. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  9. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 443
  10. 2021From Canonical Correlation Analysis to Self-supervised Graph Neural Networkscited by 138
  11. 2023SGFormer: Simplifying and Empowering Transformers for Large-Graph Representationscited by 131
  12. 2021Deep Graph Clustering via Dual Correlation Reductioncited by 97
14 more
  1. 2019Attributed Graph Clustering via Adaptive Graph Convolutioncited by 96
  2. 2024Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classificationcited by 83
  3. 2024Polynormer: Polynomial-Expressive Graph Transformer in Linear Timecited by 64
  4. 2025Position: Graph Learning Will Lose Relevance Due To Poor Benchmarkscited by 52
  5. 2023Dink-Net: Neural Clustering on Large Graphscited by 38
  6. 2021Rethinking Graph Auto-Encoder Models for Attributed Graph Clusteringcited by 37
  7. 2022A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resourcecited by 26
  8. 2024GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Datacited by 20
  9. 2024Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspectivecited by 18
  10. 2024Reliable Node Similarity Matrix Guided Contrastive Graph Clusteringcited by 12
  11. 2023Efficient High-Quality Clustering for Large Bipartite Graphscited by 11
  12. 2024End-to-end Learnable Clustering for Intent Learning in Recommendationcited by 10
  13. 2024Negative-Free Self-Supervised Gaussian Embedding of Graphscited by 5
  14. 2024Scalable and Adaptive Spectral Embedding for Attributed Graph Clusteringcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Cluster Attention for Graph Machine Learningcited by 1
  2. 2026Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clusteringcited by 0
  3. 2026SynCo: Synthetic Community-Aware Attributed Graph Generator for Graph Neural Network Benchmarkingcited by 0