其中 Graph representation learning 包括了 DeepWalk、LINE、node2vec、LightGCN;Graph contrastive learning 中包括了 SimpleGCL、DGI、GraphCL、GRACE、SGL;Graph generative and adversarial learning 中选择了 GraphGAN、AD-GCL、GraphMAE。 5.1.3 Parameter Settings. 5.1.4 Metrics. 5.2 Node Classification (RQ1) ...
论文名称:Graph Contrastive Learning with Augmentations 摘要 对于当前的图神经网络(GNNs)来说,在图结构数据上的可通用、可转移和鲁棒的表示学习仍然是一个挑战。与用于图像数据的卷积神经网络(CNNs)开发的技术不同,自我监督学习和预训练的探索较少。在本文中,我们提出了一个图对比学习(学习图数据无监督表示框架的...
Although efforts have been reported to address these two tasks, most of them rely on hand-crafted features and there is no attempt to exploit the two prediction tasks simultaneously. In this paper, we propose a novel task-aware contrastive learning algorithm to jointly predict meningioma grade ...
TracKGE: Advanced KGE using Transformer for semantic insights.Triple2Path: Transforming graphs to paths for better Transformer use.New contrastive learning for complex relations in KGE.Mask node mode: Enhancing hidden relationship detection.关键词: KeywordsKnowledge graph embeddingTransformerContrastive learnin...
To address this issue, we propose a novel multi-task contrastive learning framework that jointly optimizes two well-designed contrastive tasks: context matching and link prediction. The context matching task identifies contextual anomalies by measuring the congruence of the target node with its local ...
Self-learning techniques enable the machine learning models to enhance the efficiency of training through obtaining supervisory signals from the training dataset. In general, self-learning approaches rely on the estimation/prediction of hidden and/or unobserved parts/properties of the input data from ...
using cell type representation simplify the contrastive learning process because the number of cell type is inherently less than the number of cells. It make SCLSC a fast and straightforward framework that scales well, even when dealing with large datasets. These characteristics make SCLSC particularl...
Cai X., Huang C., Xia L. and Ren X. LightGCL: Simple yet effective graph contrastive learning for recommendation. In International Conference on Learn
三个基于Contrastive Learning进行时间序列数据特征学习的研究工作。 Time-Series Representation Learning via Temporal and Contextual ContrastingInternational Joint Conferences on Artificial Intelligence…
Final preference contrastive learning The user’s initial temporal preference and final temporal preference are not the same in the end. Preferences at different times have different importance for model prediction. Meanwhile, during the initial preference contrastive learning process, the model might capt...