Attributed Graph Subspace Clustering with Graph-Boosting

Wang Li, En Zhu, Siwei Wang, Xifeng Guo
Proceedings of the 15th Asian Conference on Machine Learning, PMLR 222:723-738, 2024.

Abstract

Attributed graph clustering groups nodes into disjoint categories with graph convolutional networks and has exhibited promising performance in various applications. However, there are two issues preventing the performance from being improved further. First, the relationships between distant nodes are generally overlooked due to the sparsity of graphs. Second, the graph convolutional networks with few layers are sensitive to noises. To address these issues, we propose Attributed Graph Subspace clustering with Graph-Boosting (AGSGB). Specifically, to deal with the first issue, an auxiliary graph is built from the feature matrix to establish the distant relationships. And to address the second issue, a subspace clustering module, famous for its robustness to noise, is introduced. Based on the auxiliary graph and the subspace clustering module, a graph enhance module and a graph refine module are constructed, together with the graph autoencoder constituting the final clustering model. By using the given graph and the refined graph built by the graph refine module, a dual guidance supervisor is designed to train the clustering model. Finally, the clustering result can be obtained by the subspace clustering module. Extensive experimental results on five public benchmark datasets validate the effectiveness of the proposed method.

Cite this Paper

BibTeX
@InProceedings{pmlr-v222-li24c, title = {Attributed Graph Subspace Clustering with Graph-Boosting}, author = {Li, Wang and Zhu, En and Wang, Siwei and Guo, Xifeng}, booktitle = {Proceedings of the 15th Asian Conference on Machine Learning}, pages = {723--738}, year = {2024}, editor = {Yanıkoğlu, Berrin and Buntine, Wray}, volume = {222}, series = {Proceedings of Machine Learning Research}, month = {11--14 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v222/li24c/li24c.pdf}, url = {https://proceedings.mlr.press/v222/li24c.html}, abstract = {Attributed graph clustering groups nodes into disjoint categories with graph convolutional networks and has exhibited promising performance in various applications. However, there are two issues preventing the performance from being improved further. First, the relationships between distant nodes are generally overlooked due to the sparsity of graphs. Second, the graph convolutional networks with few layers are sensitive to noises. To address these issues, we propose Attributed Graph Subspace clustering with Graph-Boosting (AGSGB). Specifically, to deal with the first issue, an auxiliary graph is built from the feature matrix to establish the distant relationships. And to address the second issue, a subspace clustering module, famous for its robustness to noise, is introduced. Based on the auxiliary graph and the subspace clustering module, a graph enhance module and a graph refine module are constructed, together with the graph autoencoder constituting the final clustering model. By using the given graph and the refined graph built by the graph refine module, a dual guidance supervisor is designed to train the clustering model. Finally, the clustering result can be obtained by the subspace clustering module. Extensive experimental results on five public benchmark datasets validate the effectiveness of the proposed method.} }
Endnote
%0 Conference Paper %T Attributed Graph Subspace Clustering with Graph-Boosting %A Wang Li %A En Zhu %A Siwei Wang %A Xifeng Guo %B Proceedings of the 15th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Berrin Yanıkoğlu %E Wray Buntine %F pmlr-v222-li24c %I PMLR %P 723--738 %U https://proceedings.mlr.press/v222/li24c.html %V 222 %X Attributed graph clustering groups nodes into disjoint categories with graph convolutional networks and has exhibited promising performance in various applications. However, there are two issues preventing the performance from being improved further. First, the relationships between distant nodes are generally overlooked due to the sparsity of graphs. Second, the graph convolutional networks with few layers are sensitive to noises. To address these issues, we propose Attributed Graph Subspace clustering with Graph-Boosting (AGSGB). Specifically, to deal with the first issue, an auxiliary graph is built from the feature matrix to establish the distant relationships. And to address the second issue, a subspace clustering module, famous for its robustness to noise, is introduced. Based on the auxiliary graph and the subspace clustering module, a graph enhance module and a graph refine module are constructed, together with the graph autoencoder constituting the final clustering model. By using the given graph and the refined graph built by the graph refine module, a dual guidance supervisor is designed to train the clustering model. Finally, the clustering result can be obtained by the subspace clustering module. Extensive experimental results on five public benchmark datasets validate the effectiveness of the proposed method.
APA
Li, W., Zhu, E., Wang, S. & Guo, X.. (2024). Attributed Graph Subspace Clustering with Graph-Boosting. Proceedings of the 15th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 222:723-738 Available from https://proceedings.mlr.press/v222/li24c.html.

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