Graph Contrastive Learning (GCL) has shown excellent performance in Collaborative Filtering (CF), one of the most widely used techniques in efficient recommender systems. However, existing GCL-based CF methods suffer from node degree disparity, feature oversmoothing, difficulty in distinguishing hard ...
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
1 Introduction 创新点:利用图灰盒攻击进行对比学习。 2 Graph robust contrastive learning 2.1 Background 目的:期望同一节点在两个视图的嵌入是相似的,不同节点的嵌入在两个图视图之间的嵌入是不同的。如下图所示: 通过下式计算编码器的参数θθ: argmaxθ ...
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...
Deep Graph Contrastive Representation Learning 利用节点级别的对比目标 最大化两个视图(属性级和结构级)中节点表示的一致性来学习节点表示 提高输入节点特征和高级节点嵌入之间的MI 主要侧重于对比节点级别的嵌入 正负对进行对比,王亮老师组的关于对比学习论文,还有一篇自适应的GCA,模型结构和这篇差不多 模型 首先从一...
Second, we simultaneously utilize the top-down and bottom-up information flows with relational propagations for graph representation learning. Third, to have effective early and robust detection, we implement contrastive learning on graphs with early and complete views of information propagation so that...
Here we introduce DeepSearch, a deep learning-based end-to-end database search method for tandem mass spectrometry. DeepSearch leverages a modified transformer-based encoder–decoder architecture under the contrastive learning framework. Unlike conventional methods, which rely on ion-to-ion matching, ...
SimDCL: dropout-based simple graph contrastive learning for recommendation. Complex Intell Syst. 2023:1–13. Alzubaidi L, Jebur SA, Jaber TA, Mohammed MA, Alwzwazy HA, Saihood A, Gammulle H, Santamaria J, Duan Y, Fookes C, Jurdak R. ATD Learning: A secure, smart, and decentralised...
Paper tables with annotated results for Towards Data-centric Graph Machine Learning: Review and Outlook