AGCTO: Attributed Graph Clustering With Transitive Order Convolutional Autoencoder

doi:10.1109/TETCI.2025.3526281

AGCTO: Attributed Graph Clustering With Transitive Order Convolutional Autoencoder

2025-01-01

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DOI：10.1109/TETCI.2025.3526281

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Attributed graphs, which associate vertices or edges with attributes, are essential in applications like social network analysis, recommendation systems, bioinformatics, and healthcare analysis. However, their high-dimensional and non-Euclidean nature complicates tasks such as vertex classification, community detection, graph visualization, and graph embedding. Existing techniques often inadequately integrate structural and attribute data, and reconstruction-centric models can compromise graph topology. Additionally, many methods require extensive manual hyperparameter tuning. To address these challenges, we introduce Attributed Graph Clustering with Transitive Order convolutional autoencoder (AGCTO). AGCTO uses a Graph Transitive Convolutional AutoEncoder (GTCAE) framework that integrates structural and attribute information. A key innovation is the Graph ORder Distance (GORD), capturing topological relationships to enhance clustering performance. AGCTO's unified loss function combines GTCAE loss on the original graph, GTCAE loss on a simplified graph via GORD, and a topology-preserving loss. This ensures effective fusion of attribute and structure data and preserves graph topology. Evaluations on four real-world and two synthetic attributed graphs show AGCTO's superiority in clustering accuracy, normalized mutual information, F1-score, and Q-modularity. AGCTO offers a robust solution for attributed graph clustering, maintaining graph topology and delivering superior performance.

Keywords:

Autoencoders

Feature extraction

Topology

Vectors

Fuses

Decoding

Data mining

Manuals

Image edge detection

Convolutional codes

GNNs

clustering

embedding

convolutional autoencoder

AGCTO: Attributed Graph Clustering With Transitive Order Convolutional Autoencoder

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