Graph Contrastive Learning

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Graph contrastive learning uses self-supervision to teach models which nodes, subgraphs, or whole graphs should have similar representations, reducing reliance on labeled data. Common approaches create augmented graph views and contrast their embeddings, using graph neural networks, spectral or structural signals, and objectives at multiple scales; some methods instead focus on selecting reliable positives and negatives. The learned representations support tasks such as node classification, clustering, link prediction, recommendation, and anomaly detection, including on dynamic and heterogeneous graphs. A central challenge is designing augmentations and contrastive pairs that remove noise without discarding useful structure, especially when graphs are sparse, heterophilous, or changing over time.

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37 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation
Apr 15, 2026Xiaofan Zhou, Kyumin LeeMulti-View Contrastive Learning Sequential Recommendation
Worcester Polytechnic Institute, Worcester, USA
ASPECT: Node-Level Adaptive Spectral Fusion for Graph Contrastive Learning
Apr 2, 2026Zhuolong Li, Boxue Yang, Haopeng ChenGraph Robustness Graph Contrastive Learning
School of Computer Science, Shanghai Jiao Tong University
Cross-Slice Knowledge Transfer via Masked Multi-Modal Heterogeneous Graph Contrastive Learning for Spatial Gene Expression Inference
Mar 24, 2026Zhiceng Shi, Changmiao Wang, Jun Wan +1Spatial Gene Expression Prediction Gene Expression Prediction from Histopathology Images
Yunnan University, Kunming 650500, China · Shenzhen Research Institute of Big Data, Shenzhen 518172, China · Zhongnan University of Economics and Law, Wuhan 430073, China
RaDAR: Relation-aware Diffusion-Asymmetric Graph Contrastive Learning for Recommendation
Mar 17, 2026Yixuan Huang, Jiawei Chen, Shengfan Zhang +1Graph Contrastive Learning Collaborative Filtering
National University of Defense Technology, Changsha, China · Ant Group, Chongqing, China · Tsinghua University, Beijing, China
BrainSCL: Subtype-Guided Contrastive Learning for Brain Disorder Diagnosis
Mar 11, 2026Xiaolong Li, Guiliang Guo, Guangqi Wen +6Brain Disorder Classification Contrastive Learning
Computer Science and Engineering, Northeastern University, Shenyang, China · School of Computer Science and Artificial Intelligence, Shandong Normal University, Jinan, China · AiShiWeiLai AI Research, Beijing, China +2
BrainSTR: Spatio-Temporal Contrastive Learning for Interpretable Dynamic Brain Network Modeling
Mar 10, 2026Guiliang Guo, Guangqi Wen, Lingwen Liu +6Dynamic Functional Connectivity Brain Network Analysis
Computer Science and Engineering, Northeastern University, Shenyang, China · School of Computer Science and Artificial Intelligence, Shandong Normal University, Jinan, China · Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, Fukuoka, Japan +3
CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection
Feb 24, 2026Zhongpeng Qi, Jun Zhang, Wei Li +1Time-Series Anomaly Detection Graph Contrastive Learning
Dalian Maritime University, Dalian, China · Harbin Engineering University, Harbin, China

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ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation

Xiaofan Zhou, Kyumin Lee

Worcester Polytechnic Institute, Worcester, USA

Multi-View Contrastive Learning Sequential Recommendation Contrastive Learning Multi-View Learning Graph Contrastive Learning

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Sequential recommendation has become increasingly prominent in both academia and industry, particularly in e-commerce. The primary goal is to extract user preferences from historical interaction sequences and predict items a user is likely to engage with next. Recent advances have leveraged contrastive learning and graph neural networks to learn more expressive representations from interaction histories -- graphs capture relational structure between nodes, while ID-based representations encode item-specific information. However, few studies have explored multi-view contrastive learning between ID and graph perspectives to jointly improve user and item representations, especially in settings where only interaction data is available without auxiliary information. To address this gap, we propose Multi-View Contrastive learning for sequential recommendation (MVCrec), a framework that integrates complementary signals from both sequential (ID-based) and graph-based views. MVCrec incorporates three contrastive objectives: within the sequential view, within the graph view, and across views. To effectively fuse the learned representations, we introduce a multi-view attention fusion module that combines global and local attention mechanisms to estimate the likelihood of a target user purchasing a target item. Comprehensive experiments on five real-world benchmark datasets demonstrate that MVCrec consistently outperforms 11 state-of-the-art baselines, achieving improvements of up to 14.44% in NDCG@10 and 9.22% in HitRatio@10 over the strongest baseline. Our code and datasets are available at https://github.com/sword-Lz/MMCrec.