进行实验研究,以证明 XSimGCL 是基于图增强的对应数据集的理想替代方案。 对比学习有效性证明,从sgl(Selfsupervised graph learning for recommendation)的变体开始 借鉴了最初的SGL的三种变体:SGL-ND (-ND表示节点丢弃)、SGL-ED (-ED表示边缘丢弃)和SGL-RW (-RW表示随机行走,即多层边缘丢弃)。同时为了创建一个...
对比学习-Towards Robust Graph Contrastive Learning 标签:鲁棒性、对比学习、图神经 动机 提升对抗攻击中的鲁棒性,并扩展到自监督对比学习方法中 贡献 提出了图鲁棒对比学习 (GROC),将对抗性转换整合到图形对比学习框架中,这是一种完全自监督的图算法,旨在实
XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation XSimGCL: 面向极简图对比学习的推荐框架 来源:TKDE 2022 摘要:本文是SIGIR 2022 SimGCL的改良版,为同一作者所著。在本文中,作者质疑了对比学习作为推荐的辅助任务使用管道方法与推荐主任务分离的必要性,并提出了XSimGCL框架,该框架取消...
Contrastive learning (CL) has recently been demonstrated critical in improving recommendation performance. The underlying principle of CL-based recommendation models is to ensure the consistency between representations derived from different graph augmentations of the user-item bipartite graph. This self-supe...
graph neural networks, graph structure learning, unsupervised learning, contrastive learning ABSTRACT 背景:近年来,图神经网络(GNN)作为一种成功的工具出现在各种与图相关的应用中。然而,当原始图结构中出现噪声连接时,GNN的性能会下降;此外,对显式结构的依赖使GNN无法应用于一般的非结构化场景。 研究现状:为了解决...
graph structure learning module structure bootstrapping contrastive learning module3.1 Graph LearnerGraph Learner 生成一个带参数的图邻接矩阵 ~S∈Rn×nS~∈Rn×n。本文的 Graph Learner 包括如下四种:FGP learner Attentive Learner MLP Learner GNN Learner ...
Graph neural networkContrastive learningLinear attention mechanismAs a critical technology, anomaly detection ensures the smooth operation of cloud systems while maintaining the market competitiveness of cloud service providers. However, the resource data in real-world cloud systems is predominantly unannotated...
《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》(NeurIPS 2022) GitHub: github.com/zyzisastudyreallyhardguy/Graph-Group-Discrimination [fig3]《3D Instances as 1D Kernels》(ECCV 2022) GitHub: github.com/W1zheng/DKNet [fig5] ...
Predicting lymph node metastasis using histopathological images based on multiple instance learning with deep graph convolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4837–4846 (2020). Wang, X. et al. RetCCL: clustering-guided contrastive learning for ...
在获得两个augmented graph views后,preform一个Node-level Contrastive Learning(节点级的对比学习)以最大化他们之间的MI。 Contrastive Learning Framework(对比学习框架)由3部分组成: (1)GNN-based encoder 基于GNN的编码器 (2)MLP-based projector 基于MLP的处理器,位于encoder后面,对比损失用下式计算: ...