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...
To address this challenge, we propose a novel framework called Global-local aware Heterogeneous Graph Contrastive Learning (GlaHGCL). GlaHGCL combines global and local contrastive learning to improve node embeddings in the heterogeneous graph. In particular, global contrastive learning enhances the ...
分类:Representation Learning 标签:2023,contrastive learning,empirical,graph,novel,pretraining,WWW 馒头and花卷 粉丝-93关注 -1 会员号:2578(终身会员VIP) +加关注 0 0 «上一篇:GPT-GNN: Generative Pre-Training of Graph Neural Networks »下一篇:BWT and FM-index ...
graph auto-encoders, contrastive learning.graph auto-encodersGAE (graph auto-encoders), VGAE (variational GAE) (Kipf and Welling, 2016), It uses a simple decoder to reconstruct the adjacency matrix.H=GCN(X,A)~A=f(HHT)H=GCN(X,A)A~=f(HHT)...
I think in your case you need to use a contrastive loss (either hinge or cross entropy), using examples of positive and negative edges. i think the ogbl-biokg doesn’t have features on the nodes, @rusty1s , so I thought @sophiakrix needs to add some if she wants to use a GNN. ...
Graph contrastive learning (GCL), learning the node representation by contrasting two augmented graphs in a self-supervised way, has attracted considerable attention. GCL is usually believed to learn the invariant representation. However, does this understanding always hold in practice? In this paper,...
Deep learning models can accurately predict molecular properties and help making the search for potential drug candidates faster and more efficient. Many existing methods are purely data driven, focusing on exploiting the intrinsic topology and construct
如Background所述,目前还没有任何GAE模型能够在链路预测、节点分类和图分类任务中都表现出色,即与当前图自监督学习下的最佳方法--图对比学习(GCL graph contrastive learning)模型相当(笔者注: 图对比学习GCL是目前图自监督学习SSL下的SOTA方法)。这使得这篇文章对于GAE的通用性产生了研究兴趣。这篇文章的研究主要围绕...
Learning effective graph representations in an unsupervised manner is a popular research topic in graph data analysis. Recently, contrastive learning has shown its success in unsupervised graph representation learning. However, how to avoid collapsing solutions for contrastive learning methods remains a crit...