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graph contrastive learning of modeling global-local interactions under hierarchical strategy: Application in anomaly detection

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2025年 196卷

作者： Guo, Weiwei Wang, Yang Zhou, Le Jia, Mingwei Liu, Yi Zhejiang Univ Technol Inst Proc Equipment & Control Engn Hangzhou 310023 Peoples R China Shanghai Dianji Univ Sch Elect Engn Shanghai 200240 Peoples R China Zhejiang Univ Sci & Technol Sch Automat & Elect Engn Hangzhou 310023 Zhejiang Peoples R China

Lack of labeled samples and complexity of unit interactions pose significant challenges for effective anomaly detection in complex industrial processes. This work proposes an unsupervised anomaly detection framework, hierarchical strategy-based global-local graph contrastive learning (HS-GLGCL). First, a process topology graph is constructed based on prior knowledge and then enhanced by the technique of graph augmentation. The graph contrastive learning mechanism enables the model to learn intrinsic information from unlabeled samples. Local topological information is input into the model through structural coefficients to further accurately simulate information transmission between local variables amid complex unit interactions. When global topological information is found inadequately captured, isomorphic similarity is introduced to help the model obtain embeddings that can more accurately describe data distributions. Anomalies are detected and localized by setting a reconstruction error threshold. Additionally, reconstruction dissimilarity represented by Kullback-Leibler divergence is adopted to further confirm the model's performance superiority and complete a thorough evaluation of the model's reconstruction performance. The effectiveness of HS-GLGCL is validated in two case studies on data respectively from a sugar factory and a sour water treatment system.

关键词： Anomaly detection Topological isomorphism graph contrastive learning Process knowledge Process industry

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graph contrastive learning for Multibehavior Recommendation

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年

作者： Ma, Gang-Feng Chen, Meng-Ang Yang, Xu-Hua Wen, Xilin Long, Haixia Huang, Yujiao Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China Zhejiang Univ Technol Coll Zhijiang Shaoxing 312030 Peoples R China

Multibehavior collaborative filtering recommendations can significantly alleviate data sparsity issues caused by insufficient single-behavior information, enhancing recommendation performance. However, current multibehavior recommendation methods simply concatenate different behavior representations without further exploring the interactive information between behaviors, thus limiting recommendation effectiveness. To address the limitations, we propose the graph contrastive learning for multibehavior recommendation (GCMR) model. First, we use a shared bottom to capture the connections between different behaviors of each user. Then, we introduce a GCN-based multibehavior contrastive learning approach that employs cross-layer and cross-behavior contrastive learning to capture intrabehavior and cross-behavior network interaction information, which enhances user and item representations. Additionally, we propose a multibehavior feature fusion strategy that integrates user representations (and item representations) to fully exploit latent information of different behaviors and improve network representation performance. Extensive experiments on three open-source datasets demonstrate that the GCMR model outperforms the state-of-the-art, especially on the Tmall dataset, where GCMR achieved an improvement of 19.10% in HR@10 and 16.50% in NDCG@10 over the best baseline.

关键词： contrastive learning Data mining Cross layer design Recommender systems Noise Data models graph convolutional networks Attention mechanisms User preference Singular value decomposition graph contrastive learning graph neural network multibehavior feature fusion multibehavior recommendation

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graph contrastive learning with multiple information fusion

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 268卷

作者： Wang, Xiaobao Yang, Jun Wang, Zhiqiang He, Dongxiao Zhao, Jitao Huang, Yuxiao Jin, Di Tianjin Univ Coll Intelligence & Comp Tianjin 300354 Peoples R China Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen 518107 Peoples R China George Washington Univ Washington DC 20052 USA

graph contrastive learning has been extensively studied and achieved great success in many graph downstream tasks. Currently, some works try to construct positive and negative samples in an augmented-free manner. However, most existing methods are not completely augmented-free, such as manually setting the number of similar nodes, which is an alternative augmented mode and also causes certain information loss. Thus, we propose a novel completely Augmented-Free graph contrastive learning (C-AFGCL) framework with multitask constraints. Specifically, we use the first-order neighbors of anchor nodes as positive samples and other nodes directly as negative samples, therefore fully avoiding the augmentation procedure. Then, we design a global propagation matrix and perform information propagation and aggregation to achieve global information completion. Finally, we employ attribute reconstruction to mitigate the issue of node self-information loss caused by the extensive sharing of data between samples in the preceding process. Experiments on several datasets demonstrate that our proposed model achieves outstanding results. On average, our model surpasses the best baseline based on augmentation (GRACE) by 1.05% and the best baseline without augmentation (AFGRL) by 1.06% in node classification, while also outperforming supervised methods on all networks.

关键词： graph contrastive learning graph representation learning

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graph contrastive learning of subcellular-resolution spatial transcriptomics improves cell type annotation and reveals critical molecular pathways

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BRIEFINGS IN BIOINFORMATICS 2025年第1期26卷 bbaf020页

作者： Lu, Qiaolin Ding, Jiayuan Li, Lingxiao Chang, Yi Jilin Univ Sch Artificial Intelligence Qianjin St 2699 Changchun 130010 Peoples R China Michigan State Univ Dept Comp Sci & Engn 220 Trowbridge Rd E Lansing MI 48824 USA Boston Univ Commonwealth Ave Boston MA 02215 USA Jilin Univ Int Ctr Future Sci Qianjin St 2699 Changchun 130010 Peoples R China Jilin Univ Engn Res Ctr Knowledge Driven Human Machine Intell Qianjin St 2699 Changchun 130010 Peoples R China

Imaging-based spatial transcriptomics (iST), such as MERFISH, CosMx SMI, and Xenium, quantify gene expression level across cells in space, but more importantly, they directly reveal the subcellular distribution of RNA transcripts at the single-molecule resolution. The subcellular localization of RNA molecules plays a crucial role in the compartmentalization-dependent regulation of genes within individual cells. Understanding the intracellular spatial distribution of RNA for a particular cell type thus not only improves the characterization of cell identity but also is of paramount importance in elucidating unique subcellular regulatory mechanisms specific to the cell type. However, current cell type annotation approaches of iST primarily utilize gene expression information while neglecting the spatial distribution of RNAs within cells. In this work, we introduce a semi-supervised graph contrastive learning method called Focus, the first method, to the best of our knowledge, that explicitly models RNA's subcellular distribution and community to improve cell type annotation. Focus demonstrates significant improvements over state-of-the-art algorithms across a range of spatial transcriptomics platforms, achieving improvements up to 27.8% in terms of accuracy and 51.9% in terms of F1-score for cell type annotation. Furthermore, Focus enjoys the advantages of intricate cell type-specific subcellular spatial gene patterns and providing interpretable subcellular gene analysis, such as defining the gene importance score. Importantly, with the importance score, Focus identifies genes harboring strong relevance to cell type-specific pathways, indicating its potential in uncovering novel regulatory programs across numerous biological systems.

关键词： spatial transcriptomics graph contrastive learning cell type annotation subcellular gene neighborhood network

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graph contrastive learning with node-level accurate difference

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FUNDAMENTAL RESEARCH 2025年第2期5卷 818-829页

作者： Jiao, Pengfei Yu, Kaiyan Bao, Qing Jiang, Ying Guo, Xuan Zhao, Zhidong Hangzhou Dianzi Univ Sch Cyberspace Hangzhou 310018 Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Hangzhou Dianzi Univ Data Secur Governance Zhejiang Engn Res Ctr Hangzhou 310018 Peoples R China

graph contrastive learning (GCL) has attracted extensive research interest due to its powerful ability to capture latent structural and semantic information of graphs in a self-supervised manner. Existing GCL methods commonly adopt predefined graph augmentations to generate two contrastive views. Subsequently, they design a contrastive pretext task between these views with the goal of maximizing their agreement. These methods assume the augmented graph can fully preserve the semantics of the original. However, typical data augmentation strategies in GCL, such as random edge dropping, may alter the properties of the original graph. As a result, previous GCL methods overlooked graph differences, potentially leading to difficulty distinguishing between graphs that are structurally similar but semantically different. Therefore, we argue that it is necessary to design a method that can quantify the dissimilarity between the original and augmented graphs to more accurately capture the relationships between samples. In this work, we propose a novel graph contrastive learning framework, named Accurate Difference-based Node-Level graph contrastive learning (DNGCL), which helps the model distinguish similar graphs with slight differences by learning node-level differences between graphs. Specifically, we train the model to distinguish between original and augmented nodes via a node discriminator and employ cosine dissimilarity to accurately measure the difference between each node. Furthermore, we employ multiple types of data augmentation commonly used in current GCL methods on the original graph, aiming to learn the differences between nodes under different augmentation strategies and help the model learn richer local information. We conduct extensive experiments on six benchmark datasets and the results show that our DNGCL outperforms most state-of-the-art baselines, which strongly validates the effectiveness of our model.

关键词： graph neural network graph contrastive learning Accurate difference measure Node representation learning Pretext task design

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

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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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Semi-Supervised Dual-Stream Self-Attentive Adversarial graph contrastive learning for Cross-Subject EEG-Based Emotion Recognition

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2025年第1期16卷 290-305页

作者： Ye, Weishan Zhang, Zhiguo Teng, Fei Zhang, Min Wang, Jianhong Ni, Dong Li, Fali Xu, Peng Liang, Zhen Shenzhen Univ Sch Biomed Engn Med Sch Shenzhen 518060 Peoples R China Guangdong Prov Key Lab Biomed Measurements & Ultra Shenzhen 518060 Peoples R China Harbin Inst Technol Inst Comp & Intelligence Shenzhen 518000 Peoples R China Marshall Lab Biomed Engn Shenzhen 518060 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Shenzhen Kangning Hosp Shenzhen Mental Hlth Ctr Shenzhen 518020 Peoples R China Univ Elect Sci & Technol China Clin Hosp Chengdu Brain Sci Inst MOE Key Lab Neuroinformat Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Life Sci & Technol Ctr Informat Med Chengdu 611731 Peoples R China

Electroencephalography (EEG) is an objective tool for emotion recognition with promising applications. However, the scarcity of labeled data remains a major challenge in this field, limiting the widespread use of EEG-based emotion recognition. In this paper, a semi-supervised Dual-stream Self-attentive Adversarial graph contrastive learning framework (termed as DS-AGC) is proposed to tackle the challenge of limited labeled data in cross-subject EEG-based emotion recognition. The DS-AGC framework includes two parallel streams for extracting non-structural and structural EEG features. The non-structural stream incorporates a semi-supervised multi-domain adaptation method to alleviate distribution discrepancy among labeled source domain, unlabeled source domain, and unknown target domain. The structural stream develops a graph contrastive learning method to extract effective graph-based feature representation from multiple EEG channels in a semi-supervised manner. Further, a self-attentive fusion module is developed for feature fusion, sample selection, and emotion recognition, which highlights EEG features more relevant to emotions and data samples in the labeled source domain that are closer to the target domain. Extensive experiments are conducted on four benchmark databases (SEED, SEED-IV, SEED-V, and FACED) using a semi-supervised cross-subject leave-one-subject-out cross-validation evaluation protocol. The results show that the proposed model outperforms existing methods under different incomplete label conditions with an average improvement of 2.17%, which demonstrates its effectiveness in addressing the label scarcity problem in cross-subject EEG-based emotion recognition.

关键词： Feature extraction Emotion recognition Brain modeling Electroencephalography Streams Data models Transfer learning EEG emotion recognition graph contrastive learning domain adaption semi-supervised learning

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Self-attentive Rationalization for Interpretable graph contrastive learning

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2025年第2期19卷 1-21页

作者： Li, Sihang Luo, Yanchen Zhang, An Wang, Xiang Li, Longfei Zhou, Jun Chua, Tat-seng Univ Sci & Technol China Hefei Peoples R China Natl Univ Singapore Singapore Singapore Ant Grp Hangzhou Peoples R China

graph augmentation is the key component to reveal instance-discriminative features of a graph as its rationale- an interpretation for it-in graph contrastive learning (GCL). Existing rationale-aware augmentation mechanisms in GCL frameworks roughly fall into two categories and suffer from inherent limitations: (1) non-heuristic methods with the guidance of domain knowledge to preserve salient features, which require expensive expertise and lack generality, or (2) heuristic augmentations with a co-trained auxiliary model to identify crucial substructures, which face not only the dilemma between system complexity and transformation diversitybut also the instability stemming from the co-training of two separated sub-models. Inspired by recent studies on transformers, we propose self-attentive rationale-guided GCL (SR-GCL), which integrates rationale generator and encoder together, leverages the self-attention values in transformer module as a natural guidance to delineate semantically informative substructures from both node- and edge-wise perspectives, and contrasts on rationale-aware augmented pairs. On real-world biochemistry datasets, visualization results verify the effectiveness and interpretability of self-attentive rationalization, and the performance on downstream tasks demonstrates the state-of-the-art performance of SR-GCL for graph model pre-training. Codes are available at https://***/lsh0520/SR-GCL.

关键词： Self-supervised learning interpretability graph contrastive learning self-attention mechanism

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Molecular graph contrastive learning with line graph

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PATTERN RECOGNITION 2025年 162卷

作者： Chen, Xueyuan Li, Shangzhe Liu, Ruomei Shi, Bowen Liu, Jiaheng Wu, Junran Xu, Ke Beihang Univ State Key Lab Complex & Crit Software Environm 37 Xueyuan Rd Beijing 100191 Peoples R China Cent Univ Finance & Econ Sch Stat & Math 39 South Coll Rd Beijing 100081 Peoples R China Commun Univ China Sch Journalism 1 Dingfuzhuang East St Beijing 100024 Peoples R China

Trapped by the label scarcity in molecular property prediction and drug design, graph contrastive learning (GCL) came forward. Leading contrastive learning works show two kinds of view generators, that is, random or learnable data corruption and domain knowledge incorporation. While effective, the two ways also lead to molecular semantics altering and limited generalization capability, respectively. To this end, we relate the LinE graph with MOlecular graph contrastive learning and propose a novel method termed LEMON. Specifically, by contrasting the given graph with the corresponding line graph, the graph encoder can freely encode the molecular semantics without omission. Furthermore, we present anew patch with edge attribute fusion and two local contrastive losses enhance information transmission and tackle hard negative samples. Compared with state-of-the-art (SOTA) methods for view generation, superior performance on molecular property prediction suggests the effectiveness of our proposed framework.

关键词： Molecular pre-training graph contrastive learning Dual-helix graph encoder Hard negative samples Transfer learning

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Attributed network community detection based on graph contrastive learning and multi-objective evolutionary algorithm

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NEUROCOMPUTING 2025年 636卷

作者： Liang, Yao Shu, Jian Liu, Linlan Nanchang Hangkong Univ Dept Software Nanchang 330063 Peoples R China Nanchang Hangkong Univ Dept Informat Engn Nanchang 330063 Peoples R China

Attributed network community detection holds significant research value for network structure analysis and practical applications. However, existing methods still face significant challenges in addressing the conflicts between topological structure and attribute features, as well as balancing structural tightness and attribute similarity in community detection. In light of this, we propose a community detection method based on graph contrastive learning and multi-objective evolutionary algorithm (GCL-MOEA) for attributed networks. Specifically, GCL-MOEA contains two core parts: node embedding and community detection. Considering the conflict between topological structure and attribute features, the node embedding part constructs topology-augmented and attribute-augmented views, which are utilized in a cross-view graph contrastive learning model. This model comprehensively extracts node features to obtain node embedding vectors, effectively preserving the consistency and complementarity between the structure and attributes. The community detection part utilizes clustering results of node embeddings to construct high-quality initial populations. A multi-objective evolutionary algorithm is subsequently employed to obtain community structures where nodes are tightly connected and have similar attributes. The effectiveness of the proposed method is validated on five real-world networks. Experimental results demonstrate that GCL-MOEA outperforms baselines in terms of ACC, NMI, ARI, and F1, obtaining better community detection results.

关键词： Attributed network Community detection graph contrastive learning Multi-objective evolutionary

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