TY - JOUR
T1 - AGCTO: Attributed Graph Clustering With Transitive Order Convolutional Autoencoder
AU - Xie, Ying
AU - Wang, jixiang
AU - Liu, Weihua
AU - Wang, Hao
AU - Pan, Yushan
AU - Wen, Lijie
AU - Xu, Rongbin
AU - Ding, Weiping
AU - Yang, Yun
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - GNNs
KW - clustering
KW - convolutional autoencoder
KW - embedding
UR - http://www.scopus.com/inward/record.url?scp=85215382104&partnerID=8YFLogxK
U2 - 10.1109/TETCI.2025.3526281
DO - 10.1109/TETCI.2025.3526281
M3 - Article
SN - 2471-285X
JO - IEEE Transactions on Emerging Topics in Computational Intelligence
JF - IEEE Transactions on Emerging Topics in Computational Intelligence
ER -