Cluster-guided graph attention auto-encoder

Zhiwen Zheng

doi:10.1007/s11227-025-06953-0

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Cluster-guided graph attention auto-encoder

2025-02-08

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Zhiwen Zheng

Xiaoyun Chen *

Huang, Musheng

DOI：10.1007/s11227-025-06953-0

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Abstract

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Attribute graph clustering is an important tool to analyze and understand complex networks. In recent years, graph attention auto-encoder has been applied to attribute graph clustering as a learning method for unsupervised feature representation. However, the graph attention auto-encoder only learns the feature representation of the nodes, and needs to use traditional clustering algorithms such as k-means and spectral clustering to achieve the final clustering of nodes. During the optimization process, the clustering loss cannot be fed back to the auto-encoder and the extracted features are not necessarily suitable for downstream clustering tasks because the auto-encoder model for feature learning and the clustering model are mutually independent. To overcome this problem, we propose a cluster-guided graph attention auto-encoder (CGATAE), which introduces a cluster-guided pairwise feature relationship preservation-based non-negative matrix factorization model (FR-NMF) into the graph attention auto-encoder. The model CGATAE obtains the final clustering results while learning the cluster-oriented node feature representation. Experiments on five public attribute graph datasets verify the effectiveness of the CGATAE model, and its clustering quality is significantly better than the original graph attention auto-encoder model.

Keywords:

Graph clustering

Feature representations

Matrix factorization

Graph auto-encoder

Feature relationship preservation