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GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling

Science China(Information Sciences) 2025年第1期68卷 145-158页

作者： Wei JU Yiyang GU Zhengyang MAO Ziyue QIAO Yifang QIN Xiao LUO Hui XIONG Ming ZHANG School of Computer Science National Key Laboratory for Multimedia Information ProcessingPeking University Artificial Intelligence Thrust The Hong Kong University of Science and Technology Department of Computer Science University of California

Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown promising performance for representation learning on graphs, which train models by maximizing agreement between original graphs and their augmented views(i.e., positive views). Unfortunately, these methods usually involve pre-defined augmentation strategies based on the knowledge of human experts. Moreover, these strategies may fail to generate challenging positive views to provide sufficient supervision signals. In this paper, we present a novel approach named graph pooling contrast(GPS) to address these *** by the fact that graph pooling can adaptively coarsen the graph with the removal of redundancy, we rethink graph pooling and leverage it to automatically generate multi-scale positive views with varying emphasis on providing challenging positives and preserving semantics, i.e., strongly-augmented view and weakly-augmented view. Then, we incorporate both views into a joint contrastive learning framework with similarity learning and consistency learning, where our pooling module is adversarially trained with respect to the encoder for adversarial robustness. Experiments on twelve datasets on both graph classification and transfer learning tasks verify the superiority of the proposed method over its counterparts.

关键词： graph representation learning graph neural networks graph contrastive learning graph augmentations graph pooling

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Residual Convolutional graph Neural Network with Subgraph Attention pooling

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Tsinghua Science and Technology 2022年第4期27卷 653-663页

作者： Yutai Duan Jianming Wang Haoran Ma Yukuan Sun Information and Communication Engineering Department Tiangong UniversityTianjin 300387China Computer Science Department Tiangong UniversityTianjin 300387China Software Engineering Department Tiangong UniversityTianjin 300387China Center for Engineering Intership and Training Tiangong UniversityTianjin 300387China

The pooling operation is used in graph classification tasks to leverage hierarchical structures preserved in data and reduce computational ***,pooling shrinkage discards graph details,and existing pooling methods may lead to the loss of key classification *** this work,we propose a residual convolutional graph neural network to tackle the problem of key classification features ***,our contributions are threefold:(1)Different from existing methods,we propose a new strategy to calculate sorting values and verify their importance for graph *** strategy does not only use features of simple nodes but also their neighbors for the accurate evaluation of its importance.(2)We design a new graph convolutional layer architecture with the residual *** feeding discarded features back into the network architecture,we reduce the probability of losing critical features for graph classification.(3)We propose a new method for graph-level *** messages for each node are aggregated separately,and then different attention levels are assigned to each node and merged into a graph-level representation to retain structural and critical information for *** experimental results show that our method leads to state-of-the-art results on multiple graph classification benchmarks.

关键词： graph neural network graph pooling information loss

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