Graph Self-Supervised Learning: A Surveyieeexplore.ieee.org/abstract/document/9770382 PDF: https://arxiv.org/pdf/2103.00111.pdfarxiv.org/pdf/2103.00111.pdf 1. 背景与概括 图的深度学习作为一个热门领域引起了广泛的研究兴趣,但是由于当前研究大多集中在半监督或者监督学习上,存在标签依赖严重、泛...
此类方法就是随机的在图中插入或删除一定比例的边[9], [38], [51], [68], [73], [74],可以被形式化的表示为t_i(A) = M_1 \circ A + M_2 \circ (1-A),其中M_1, M_2分别表示边删除和插入矩阵,通过M_1, M_2随机mask掉A和(1-A)中等于1的元素来实现(M_1随机mask把一些原有边变为0...
【图深度自监督学习Philips S. Yu团队重磅新作】Graph Self-Supervised Learning: A Survey,程序员大本营,技术文章内容聚合第一站。
Cluster-aware graph neural networks for unsupervised graph representation learning. arxiv preprint. Self-supervised graph transformer on large-scale molecular data. NeurIPS, 2020.
图上的深度学习最近引起了人们的极大兴趣。然而,大多数工作都集中在(半)监督学习上,导致存在标签依赖重、泛化能力差和鲁棒性弱等缺点。为了解决这些问题,自监督学习 (SSL) 通过精心设计的借口任务提取信息知识,而不依赖于手动标签,已成为图数据的一种有前途和趋势的学习范式。与计算机视觉和自然语言处理等其他领域中...
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...
Hierarchical Molecular Graph Self-Supervised Learning for property prediction Article Open access 17 February 2023 A knowledge-guided pre-training framework for improving molecular representation learning Article Open access 21 November 2023 Data availability All data used in this paper are publicly av...
In this paper, we introduce a new self-supervised graph representation learning method DGB. DGB relies on two neural networks: online network and target network, and the input of each neural network is an augmentation of the initial graph. With the help of the bootstrapping process, the onlin...
本文的贡献主要… stephinwhite30 Graph Self-Supervised Learning: A Survey-对比学习方法 AI知其然发表于图神经网络... Graph Attention Network (GAT)论文分享 周明发表于水木学者 Robustness of deep learning models on graphs: A survey图深度学习鲁棒性综述 数据的小米...发表于图神经网络......
learningprobabilistic distributions of graph structure (i.e., probabilistic modeling methods) 略 2.1.2 Optimal Feature Learning 与结构优化相比,图特征优化的研究还处于起步阶段[16][17][5]。 2.2 GraphDA for Low-Resource Graph Learning 2.2.1 Graph Self-Supervised Learning ...