Graph Contrastive Learning With Adaptive Augmentation

By Editorial Team · October 1, 2026 · 8 min read

Graph Contrastive Learning With Adaptive Augmentation Information Guide

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Important Facts

Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices News
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Recent Updates

KDD 2023 - Adaptive Graph Contrastive Learning for Recommendation News
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[AAAI 2022] AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators [Full]
[AAAI 2022] AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators [Full]
Martina Neuman: Consistency of augmentation graph & network approximability in contrastive learning
Martina Neuman: Consistency of augmentation graph & network approximability in contrastive learning
[AAAI 2022] AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators [1 min]
[AAAI 2022] AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators [1 min]
AISTATS 2023 - Spectral Augmentations for Graph Contrastive Learning
AISTATS 2023 - Spectral Augmentations for Graph Contrastive Learning
Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Aug
Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Aug
Adaptive Fusion of Multi-View for Graph Contrastive Recommendation
Adaptive Fusion of Multi-View for Graph Contrastive Recommendation
ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs
ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs
Dual Space Graph Contrastive Learning
Dual Space Graph Contrastive Learning
Contrastive Learning - 5 Minutes with Cyrill
Contrastive Learning - 5 Minutes with Cyrill
[NeurIPS2022]10,000 times faster! (Rethinking and scaling up Graph contrastive learning)
[NeurIPS2022]10,000 times faster! (Rethinking and scaling up Graph contrastive learning)
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation

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Last Updated: October 1, 2026

Summary

Information NeurIPS 2021 | ADGCL : Adversarial Graph Augmentation to Improve Graph Contrastive News
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Summary

Authors: Yanqiao Zhu: Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences; Yichen ... Yangqin Jiang, University of Hong Kong. AAAI 2022 Poster Paper Presentation: AutoGCL: Automated Authors: Miyai, Atsuyuki*; Yu, Qing; Ikami, Daiki; Irie, Go; Aizawa, Kiyoharu Description: Rotation is frequently listed as a candidate ... Jing Feng introduces NLGCL, a recommendation-focused

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