Bridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clustering
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).
698bd6a96052d3bed963097a
Yunhui Liu
claimed_verified
2026-02-11T11:15:22.049Z
63b91ec5f270ad02f61327b0
https://cdn-avatars.huggingface.co/v1/production/uploads/1673076377130-noauth.jpeg
Yunhui Liu
Cloudy1225
user
Cloudy1225
698bd6a96052d3bed963097b
Pengyu Qiu
698bd6a96052d3bed963097c
Yu Xing
698bd6a96052d3bed963097d
Yongchao Liu
698bd6a96052d3bed963097e
Peng Du
698bd6a96052d3bed963097f
Chuntao Hong
698bd6a96052d3bed9630980
Jiajun Zheng
698bd6a96052d3bed9630981
Tao Zheng
698bd6a96052d3bed9630982
Tieke He
Bridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clustering
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).
PyAGC presents a production-ready benchmark and library for attributed graph clustering that addresses limitations of current research through scalable, memory-efficient implementations and comprehensive evaluation protocols.
attributed graph clustering graph-structured data structural topology node attributes unsupervised task fraud detection user segmentation homophily full-batch training mini-batch implementations Encode-Cluster-Optimize framework unsupervised structural metrics efficiency profiling high-stakes industrial workflows