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

Yunhui Liu; Pengyu Qiu; Yu Xing; Yongchao Liu; Peng Du; Chuntao Hong; Jiajun Zheng; Tao Zheng

cs.LGFeb 9, 2026

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

Authors: Yunhui Liu, Pengyu Qiu, Yu Xing, Yongchao Liu, Peng Du, Chuntao Hong, Jiajun Zheng, Tao Zheng,

Organizations: Nanjing University & Ant Group, Nanjing, China · Ant Group, Hangzhou, China · Nanjing University, Nanjing, China · Ant Group, Beijing, China ·

Abstract

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). We invite readers to our latest survey (https://arxiv.org/abs/2603.20829) for the unified framework and industrial perspective that complement this benchmark.

Figures & tables

Figure 2: Overview of our benchmark. It unifies (1) a diverse data atlas spanning academic and industrial domains, (2) the modular Encode-Cluster-Optimize methodology with memory-efficient mini-batch support, and (3) a holistic evaluation protocol covering supervised alignment, structural quality, and efficiency.

Scale	Dataset	Domain	#Nodes	#Edges	Avg. Deg.	#Feat.	Feat. Type	#Clus.	$\mathcal{H}_{e}$	$\mathcal{H}_{n}$
Tiny	Cora	Citation	2,708	10,556	3.9	1,433	Textual	7	0.81	0.83
Tiny	Photo	Co-purchase	7,650	238,162	31.1	745	Textual	8	0.83	0.84
Small	Physics	Co-author	34,493	495,924	14.4	8,415	Textual	5	0.93	0.92
	HM	Co-purchase	46,563	21,461,990	460.9	120	Tabular	21	0.16	0.35
	Flickr	Social	89,250	899,756	10.1	500	Textual	7	0.32	0.32
Medium	ArXiv	Citation	169,343	1,166,243	6.9	128	Textual	40	0.65	0.64

Table 1: Summary of the benchmark datasets. The collection is categorized by scale, spanning five orders of magnitude from Tiny to Massive. The datasets cover a wide range of domains and feature types (Textual, Tabular).

\mathcal{H}_{e}

and

\mathcal{H}_{n}

denote edge homophily and node homophily, respectively.

Method	Venue	Encode ( $\mathcal{E}$ )	Cluster ( $\mathcal{C}$ )	Optimize ( $\mathcal{O}$ )	Core Objective	Complexity
Non-Parametric & Decoupled Methods
AGC [ 58 ]	IJCAI’19	Fixed Filter	Discrete (Spectral)	Decoupled	Adaptive Smoothing	$O(N^{2})$
SSGC [ 60 ]	ICLR’21	Adaptive Filter	Discrete (KMeans)	Decoupled	Markov Diffusion	$O(N+M)$
NAFS [ 57 ]	ICML’22	Adaptive Filter	Discrete (KMeans)	Decoupled	Smoothing + Ensemble	$O(N+M)$
SAGSC [ 8 ]	AAAI’23	Fixed Filter	Discrete (Subspace)	Decoupled	Self-Expressive Subspace	$O(N+M)$
MS2CAG [ 18 ]	KDD’25	Fixed Filter	Discrete (SNEM [ 53 ] )	Decoupled	Rank-Constrained SVD	$O(N+M)$

Table 2: A taxonomy of representative Attributed Graph Clustering methods under the Encode-Cluster-Optimize framework.

\mathcal{E}

: Encoder Type (Parametric vs. Non-Parametric);

\mathcal{C}

: Clusterer Type (Differentiable vs. Discrete);

\mathcal{O}

: Optimization Strategy (Joint vs. Decoupled). Complexity denotes the time/space complexity, where

N=|\mathcal{V}|

and

M=|\mathcal{E}|

O(N+M)

indicates linear scalability on sparse graphs, while

O(N^{2})

indicates quadratic bottlenecks.

Category	Method	Cora		Photo		Physics		HM		Flickr		ArXiv
Category	Method	Mem(MB)	Time(s)	Mem(GB)	Time(s)	Mem(GB)	Time(m)	Mem(GB)	Time(m)	Mem(GB)	Time(m)	Mem(GB)	Time(m)
Traditional	KMeans	-	0.28	-	0.69	-	0.19	-	0.05	-	0.08	-	0.32
Traditional	Node2Vec	46.0	24.57	0.07	27.96	0.16	15.52	0.19	1.50	0.36	20.97	0.49	5.05
Non- Parametric	SSGC	-	0.56	-	0.62	-	2.25	-	0.04	-	0.10	-	0.17
	SAGSC	-	0.86	-	1.02	-	2.13	-	0.07	-	0.04	-	0.28
	MS2CAG	-	0.78	-	0.85	-	0.15	-	0.03	-	0.03	-	0.06

Table 6: Efficiency profiling results for smaller datasets. Mem: Peak GPU Memory, Time: Total Training and Clustering Time.

Category	Method	Reddit		MAG		Pokec		Products		WebTopic		Papers100M
Category	Method	Mem(GB)	Time(m)	Mem(GB)	Time(m)	Mem(GB)	Time(m)	Mem(GB)	Time(m)	Mem(GB)	Time(m)	Mem(GB)	Time(h)
Traditional	KMeans	-	0.27	-	6.59	-	3.09	-	5.87	-	3.98	-	0.05
Traditional	Node2Vec	0.70	22.67	1.90	77.58	4.04	236.53	15.05	64.94	11.80	75.92	7.20	12.38
Non- Parametric	SSGC	-	5.36	-	6.51	-	4.43	-	8.05	-	10.38	-	-
	SAGSC	-	5.99	-	23.35	-	4.72	-	5.29	-	2.77	-	-
	MS2CAG	-	0.16	-	15.10	-	13.68	-	1.38	-	16.81	-	-

Table 7: Efficiency profiling results for larger datasets. Mem: Peak GPU Memory, Time: Total Training and Clustering Time.

Graph Clustering

Graph clustering groups network nodes by connectivity, attributes, or shared roles to reveal communities and other mesoscale structure.

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

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State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China. · the School of Computing, National University of Singapore, Singapore. · Ant Group, Hangzhou, China.+2

Graph Clustering

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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.

Abstract

Graph Clustering

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