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Open Access

ARTICLE

Relation-Aware Adaptive Anchor Spectral Clustering for Large-Scale Attributed and Heterogeneous Graphs

Xiangmin Liu1,2, Weizhi Xiong1,*, Wei Zhang1, Taojian Luo1, Xiu Yao1, Jian Hu1, Peng Peng2,*
1 School of Information Engineering, Gannan College of Science and Technology, Ganzhou, China
2 College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
* Corresponding Authors: Weizhi Xiong. Email: mrxiongwz@foxmail.com; Peng Peng. Email: hnu16pp@hnu.edu.cn

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.087484

Received 07 June 2026; Accepted 17 August 2026; Published online 11 September 2026

Abstract

Spectral clustering (SC) effectively captures non-convex and graph-structured cluster patterns, but its application to large-scale attributed and heterogeneous graphs is hindered by the high costs of similarity graph construction and Laplacian eigendecomposition, as well as by insufficient use of relation semantics. To address these challenges, we propose Relation-aware Adaptive Anchor Spectral Clustering (RAASC). RAASC first identifies relation-aware local components in relation-specific subgraphs and allocates anchor budgets according to component difficulty. It then constructs an adaptive, load-aware sample-anchor graph by jointly considering node uncertainty, relation compatibility, and anchor-load balance. Using the resulting sparse sample-anchor matrices, RAASC builds a compact relation-aware reduced anchor graph and performs spectral learning, anchor-level discretization, and label recovery in the anchor space. This design avoids full similarity graph construction and reduces the scale of eigendecomposition while preserving heterogeneous structural information. Experiments on DBLP, IMDB, and OGBN-MAG show that RAASC achieves the highest mean Accuracy (ACC), Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI) among the representative clustering and spectral clustering baselines. Efficiency and scalability results further demonstrate that RAASC maintains practical runtime and memory usage on large-scale heterogeneous graphs and achieves a 3.72-fold speedup using 16 workers.

Keywords

Spectral clustering; anchor-based clustering; heterogeneous graphs; distributed clustering; large-scale graph mining; reduced graph learning

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