Adaptive negative representations for graph contrastive learning
Graph contrastive learning (GCL) has emerged as a promising paradigm for learning graph representations. Recently, the idea of hard negatives is introduced to GCL, which can provide more challenging self-supervised objectives and alleviate over-fitting issues. These methods use different graphs in t...
Main Authors: | Qi Zhang, Cheng Yang, Chuan Shi |
---|---|
Format: | Article |
Language: | English |
Published: |
KeAi Communications Co. Ltd.
2024-01-01
|
Series: | AI Open |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2666651023000219 |
Similar Items
-
Asymmetric Graph Contrastive Learning
by: Xinglong Chang, et al.
Published: (2023-10-01) -
Neural Graph Similarity Computation with Contrastive Learning
by: Shengze Hu, et al.
Published: (2022-07-01) -
Graph Neural Network-Guided Contrastive Learning for Sequential Recommendation
by: Xing-Yao Yang, et al.
Published: (2023-06-01) -
Graph contrast learning for recommendation based on relational graph convolutional neural network
by: Xiaoyang Liu, et al.
Published: (2024-10-01) -
A Lightweight Method for Graph Neural Networks Based on Knowledge Distillation and Graph Contrastive Learning
by: Yong Wang, et al.
Published: (2024-06-01)