Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning

Yuhao Wu, Jiangchao Yao, Bo Han, Lina Yao, Tongliang Liu
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:53928-53943, 2024.

Abstract

While Positive-Unlabeled (PU) learning is vital in many real-world scenarios, its application to graph data still remains under-explored. We unveil that a critical challenge for PU learning on graph lies on the edge heterophily, which directly violates the $\textit{irreducibility assumption}$ for $\textit{Class-Prior Estimation}$ (class prior is essential for building PU learning algorithms) and degenerates the latent label inference on unlabeled nodes during classifier training. In response to this challenge, we introduce a new method, named $\textit{$\underline{G}$raph $\underline{P}$U Learning with $\underline{L}$abel Propagation Loss}$ (GPL). Specifically, GPL considers learning from PU nodes along with an intermediate heterophily reduction, which helps mitigate the negative impact of the heterophilic structure. We formulate this procedure as a bilevel optimization that reduces heterophily in the inner loop and efficiently learns a classifier in the outer loop. Extensive experiments across a variety of datasets have shown that GPL significantly outperforms baseline methods, confirming its effectiveness and superiority.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-wu24ad, title = {Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning}, author = {Wu, Yuhao and Yao, Jiangchao and Han, Bo and Yao, Lina and Liu, Tongliang}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {53928--53943}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/wu24ad/wu24ad.pdf}, url = {https://proceedings.mlr.press/v235/wu24ad.html}, abstract = {While Positive-Unlabeled (PU) learning is vital in many real-world scenarios, its application to graph data still remains under-explored. We unveil that a critical challenge for PU learning on graph lies on the edge heterophily, which directly violates the $\textit{irreducibility assumption}$ for $\textit{Class-Prior Estimation}$ (class prior is essential for building PU learning algorithms) and degenerates the latent label inference on unlabeled nodes during classifier training. In response to this challenge, we introduce a new method, named $\textit{$\underline{G}$raph $\underline{P}$U Learning with $\underline{L}$abel Propagation Loss}$ (GPL). Specifically, GPL considers learning from PU nodes along with an intermediate heterophily reduction, which helps mitigate the negative impact of the heterophilic structure. We formulate this procedure as a bilevel optimization that reduces heterophily in the inner loop and efficiently learns a classifier in the outer loop. Extensive experiments across a variety of datasets have shown that GPL significantly outperforms baseline methods, confirming its effectiveness and superiority.} }
Endnote
%0 Conference Paper %T Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning %A Yuhao Wu %A Jiangchao Yao %A Bo Han %A Lina Yao %A Tongliang Liu %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-wu24ad %I PMLR %P 53928--53943 %U https://proceedings.mlr.press/v235/wu24ad.html %V 235 %X While Positive-Unlabeled (PU) learning is vital in many real-world scenarios, its application to graph data still remains under-explored. We unveil that a critical challenge for PU learning on graph lies on the edge heterophily, which directly violates the $\textit{irreducibility assumption}$ for $\textit{Class-Prior Estimation}$ (class prior is essential for building PU learning algorithms) and degenerates the latent label inference on unlabeled nodes during classifier training. In response to this challenge, we introduce a new method, named $\textit{$\underline{G}$raph $\underline{P}$U Learning with $\underline{L}$abel Propagation Loss}$ (GPL). Specifically, GPL considers learning from PU nodes along with an intermediate heterophily reduction, which helps mitigate the negative impact of the heterophilic structure. We formulate this procedure as a bilevel optimization that reduces heterophily in the inner loop and efficiently learns a classifier in the outer loop. Extensive experiments across a variety of datasets have shown that GPL significantly outperforms baseline methods, confirming its effectiveness and superiority.
APA
Wu, Y., Yao, J., Han, B., Yao, L. & Liu, T.. (2024). Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:53928-53943 Available from https://proceedings.mlr.press/v235/wu24ad.html.

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