Graph neural networksSelf-supervised learningEnhance-attentionSession-based recommendation is a challenging task which predicts the next click based on the short-term behavior of anonymous users. Compared to other recommendation models, session-based recommendations are more difficult due to the limited ...
自监督-SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling 标签:自监督、图神经网络、对比学习 动机 在真实世界中许多数据大部分是有没有标签的,而打上标签的是需要很大花费的 现存的对比学习
网上已有这篇论文的一些讲解,如有讲的不清楚的地方,可移至其他博客。 持续更新中--- Title: Self supervised graph neural network for improving electroencephalographic analysis 作者:Siyi Tang, …
论文标题:Self-supervised Graph Neural Networks without explicit negative sampling 论文作者:Zekarias T. Kefato, Sarunas Girdzijauskas 论文来源:2021, WWW 论文地址:download 论文代码:download 1 Introduction 本文核心贡献: 使用孪生网络隐式实现对比学习; ...
In this paper we train neural network-based graph architectures by means of self-supervised auxiliary tasks in a multi-task framework, similarly to [51]. Considering the promising results of the GCN, we decided to experiment this framework in semi-supervised classification problems on graphs, emplo...
Semi-Supervised and Self-Supervised Classification with Multi-View Graph Neural Networks 小源 微信公众号:小源笔迹 来自专栏 · 图算法-图神经网络-图计算 2 人赞同了该文章 摘要 图神经网络在图结构数据中取得了很好的效果但是大多数的模型使用的还是叫浅层的结构,当模型层数加深时很容易过平滑。本文基于多...
Self-Supervised Graph Neural Networks for Sequential Recommendation Yuxi Liu, Lianghao Xia, Chao Huang*SIGIR2024 * denotes corresponding author In this paper, we propose a novel framework called Self-Supervised Graph Neural Network (SelfGNN) for sequential recommendation. TheSelfGNNframework encodes short...
Official repository for ICLR'23 paper: Multi-task Self-supervised Graph Neural Network Enable Stronger Task Generalization - jumxglhf/ParetoGNN
https://github.com/LirongWu/awesome-graph-self-supervised-learning 近些年来,图上的深度学习在各种任务上取得了显著的成功,而这种成功在很大程度上依赖于海量的、精心标注的数据。然而,精确的标注通常非常昂贵和耗时。为了解决这个问题,自监督学习(Self-supervised Learning,SSL)正在成为一种全新的范式,通过精心设计的...
这篇paper是21年发表在ICML上的工作,主要提出了一个新颖的全图自监督表示学习框架(GraphLoG),除了保留传统图学习所关注的局部相似性之外,还引入了层次原型来捕获全局语义集群(全局-局部视角)。然后文章采用EM算法来学习这个模型,在化学生物等几个领域基准集上获得了不错的效果。 文章链接: https://proceedings.mlr....