Graph Self-supervised Learning via Proximity Distribution Minimization

Tianyi Zhang, Zhenwei Dai, Zhaozhuo Xu, Anshumali Shrivastava
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2498-2508, 2023.

Abstract

Self-supervised learning (SSL) for graphs is an essential problem since graph data are ubiquitous and labeling can be costly. We argue that existing SSL approaches for graphs have two limitations. First, they rely on corruption techniques such as node attribute perturbation and edge dropping to generate graph views for contrastive learning. These unnatural corruption techniques require extensive tuning efforts and provide marginal improvements. Second, the current approaches require the computation of multiple graph views, which is memory and computationally inefficient. These shortcomings of graph SSL call for a corruption-free single-view learning approach, but the strawman approach of using neighboring nodes as positive examples suffers two problems: it ignores the strength of connections between nodes implied by the graph structure on a macro level, and cannot deal with the high noise in real-world graphs. We propose Proximity Divergence Minimization (PDM), a corruption-free single-view graph SSL approach that overcomes these problems by leveraging node proximity to measure connection strength and denoise the graph structure. Through extensive experiments, we show that PDM achieves up to 4.55% absolute improvement in ROC-AUC on graph SSL tasks over state-of-the-art approaches while being more memory efficient. Moreover, PDM even outperforms supervised training on node classification tasks of ogbn-proteins dataset. Our code is publicly available.

Cite this Paper

BibTeX
@InProceedings{pmlr-v216-zhang23d, title = {Graph Self-supervised Learning via Proximity Distribution Minimization}, author = {Zhang, Tianyi and Dai, Zhenwei and Xu, Zhaozhuo and Shrivastava, Anshumali}, booktitle = {Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence}, pages = {2498--2508}, year = {2023}, editor = {Evans, Robin J. and Shpitser, Ilya}, volume = {216}, series = {Proceedings of Machine Learning Research}, month = {31 Jul--04 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v216/zhang23d/zhang23d.pdf}, url = {https://proceedings.mlr.press/v216/zhang23d.html}, abstract = {Self-supervised learning (SSL) for graphs is an essential problem since graph data are ubiquitous and labeling can be costly. We argue that existing SSL approaches for graphs have two limitations. First, they rely on corruption techniques such as node attribute perturbation and edge dropping to generate graph views for contrastive learning. These unnatural corruption techniques require extensive tuning efforts and provide marginal improvements. Second, the current approaches require the computation of multiple graph views, which is memory and computationally inefficient. These shortcomings of graph SSL call for a corruption-free single-view learning approach, but the strawman approach of using neighboring nodes as positive examples suffers two problems: it ignores the strength of connections between nodes implied by the graph structure on a macro level, and cannot deal with the high noise in real-world graphs. We propose Proximity Divergence Minimization (PDM), a corruption-free single-view graph SSL approach that overcomes these problems by leveraging node proximity to measure connection strength and denoise the graph structure. Through extensive experiments, we show that PDM achieves up to 4.55% absolute improvement in ROC-AUC on graph SSL tasks over state-of-the-art approaches while being more memory efficient. Moreover, PDM even outperforms supervised training on node classification tasks of ogbn-proteins dataset. Our code is publicly available.} }
Endnote
%0 Conference Paper %T Graph Self-supervised Learning via Proximity Distribution Minimization %A Tianyi Zhang %A Zhenwei Dai %A Zhaozhuo Xu %A Anshumali Shrivastava %B Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2023 %E Robin J. Evans %E Ilya Shpitser %F pmlr-v216-zhang23d %I PMLR %P 2498--2508 %U https://proceedings.mlr.press/v216/zhang23d.html %V 216 %X Self-supervised learning (SSL) for graphs is an essential problem since graph data are ubiquitous and labeling can be costly. We argue that existing SSL approaches for graphs have two limitations. First, they rely on corruption techniques such as node attribute perturbation and edge dropping to generate graph views for contrastive learning. These unnatural corruption techniques require extensive tuning efforts and provide marginal improvements. Second, the current approaches require the computation of multiple graph views, which is memory and computationally inefficient. These shortcomings of graph SSL call for a corruption-free single-view learning approach, but the strawman approach of using neighboring nodes as positive examples suffers two problems: it ignores the strength of connections between nodes implied by the graph structure on a macro level, and cannot deal with the high noise in real-world graphs. We propose Proximity Divergence Minimization (PDM), a corruption-free single-view graph SSL approach that overcomes these problems by leveraging node proximity to measure connection strength and denoise the graph structure. Through extensive experiments, we show that PDM achieves up to 4.55% absolute improvement in ROC-AUC on graph SSL tasks over state-of-the-art approaches while being more memory efficient. Moreover, PDM even outperforms supervised training on node classification tasks of ogbn-proteins dataset. Our code is publicly available.
APA
Zhang, T., Dai, Z., Xu, Z. & Shrivastava, A.. (2023). Graph Self-supervised Learning via Proximity Distribution Minimization. Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 216:2498-2508 Available from https://proceedings.mlr.press/v216/zhang23d.html.

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