Unsupervised Multi-view Nonlinear Graph Embedding

Jiaming Huang, Zhao Li, Vincent W. Zheng, Wen Wen, Yifan Yang, Yuanmi Chen
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:318-327, 2018.

Abstract

In this paper, we study the unsupervised multi-view graph embedding (UMGE) prob- lem, which aims to learn graph embedding from multiple perspectives in an unsupervised manner. However, the vast majority of multi- view learning work focuses on non-graph data, and surprisingly there are limited work on UMGE. By systematically analyzing different existing methods for UMGE, we discover that cross-view and nonlinearity play a vital role in efficiently improving graph embedding qual- ity. Motivated by this concept, we develop an unsupervised Multi-viEw nonlineaR Graph Embedding (MERGE) approach to model re- lational multi-view consistency. Experimen- tal results on five benchmark datasets demon- strate that MERGE significantly outperforms the state-of-the-art baselines in terms of accu- racy in node classification tasks without sacri- ficing the computational efficiency.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-huang18a, title = {Unsupervised Multi-view Nonlinear Graph Embedding}, author = {Huang, Jiaming and Li, Zhao and Zheng, Vincent W. and Wen, Wen and Yang, Yifan and Chen, Yuanmi}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {318--327}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/huang18a/huang18a.pdf}, url = {https://proceedings.mlr.press/r16/huang18a.html}, abstract = {In this paper, we study the unsupervised multi-view graph embedding (UMGE) prob- lem, which aims to learn graph embedding from multiple perspectives in an unsupervised manner. However, the vast majority of multi- view learning work focuses on non-graph data, and surprisingly there are limited work on UMGE. By systematically analyzing different existing methods for UMGE, we discover that cross-view and nonlinearity play a vital role in efficiently improving graph embedding qual- ity. Motivated by this concept, we develop an unsupervised Multi-viEw nonlineaR Graph Embedding (MERGE) approach to model re- lational multi-view consistency. Experimen- tal results on five benchmark datasets demon- strate that MERGE significantly outperforms the state-of-the-art baselines in terms of accu- racy in node classification tasks without sacri- ficing the computational efficiency.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Unsupervised Multi-view Nonlinear Graph Embedding %A Jiaming Huang %A Zhao Li %A Vincent W. Zheng %A Wen Wen %A Yifan Yang %A Yuanmi Chen %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-huang18a %I PMLR %P 318--327 %U https://proceedings.mlr.press/r16/huang18a.html %V R16 %X In this paper, we study the unsupervised multi-view graph embedding (UMGE) prob- lem, which aims to learn graph embedding from multiple perspectives in an unsupervised manner. However, the vast majority of multi- view learning work focuses on non-graph data, and surprisingly there are limited work on UMGE. By systematically analyzing different existing methods for UMGE, we discover that cross-view and nonlinearity play a vital role in efficiently improving graph embedding qual- ity. Motivated by this concept, we develop an unsupervised Multi-viEw nonlineaR Graph Embedding (MERGE) approach to model re- lational multi-view consistency. Experimen- tal results on five benchmark datasets demon- strate that MERGE significantly outperforms the state-of-the-art baselines in terms of accu- racy in node classification tasks without sacri- ficing the computational efficiency. %Z Reissued by PMLR on 04 October 2026.
APA
Huang, J., Li, Z., Zheng, V.W., Wen, W., Yang, Y. & Chen, Y.. (2018). Unsupervised Multi-view Nonlinear Graph Embedding. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:318-327 Available from https://proceedings.mlr.press/r16/huang18a.html. Reissued by PMLR on 04 October 2026.

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