GAE (graph autoencoder) carbon 随波逐流 1 人赞同了该文章 关于图自编码器的起源,这一篇文章给出了详细的介绍:zhuanlan.zhihu.com/p/11 这里便不再多做赘述,给出具体的 Encoder 和 Decoder 的定义。 Encoder : GCN (1)Z=GCN(X,A) GCN 具体为两层: (2)GCN(X,A)=A^ReLU(A^XW0)W1 其中A 为标...
The GAE Problem and GraphMAE GraphMAE的整体架构如上图所示。其核心思想是掩蔽节点的特征重建。本文引入了一种使用GNN的重掩码解码策略,而不是在GAE中广泛使用的MLP,作为增强GraphMAE的解码器。为了得到一个鲁棒重建,本文还提出使用尺度余弦误差作为准则。下图总结了GraphMAE和现有GAE之间的技术差异。 具体来说,enco...
graph auto-encodersMost recent graph clustering methods have resorted to Graph Auto-Encoders (GAEs) to perform joint clustering and embedding learning. However, two critical issues have been overlooked. First, the accumulative error, inflicted by learning from noisy clustering assignments, degrades ...
论文标题:GraphMAE: Self-Supervised Masked Graph Autoencoders论文作者:Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, Jie Tang论文来源:2022, KDD论文地址:download 论文代码:download 1 IntroductionGAE 研究困难之处:...
MaskGAE: Masked Graph Modeling Meets Graph Autoencoders GraphMAE: Self-Supervised Masked Graph Autoencoders 自监督学习（Self-Supervised Learning, SSL）在图深度学习领域取得了显著进展。通过在图数据上进行预训练，SSL 方法能够从无标签数据中发掘出有效的特征表示。生成式和对比式的自监督学习方法...
而生成式自监督学习可以避免上述依赖关系。生成式自监督学习能够重构数据本身的特征和信息。在自然语言处理(NLP)中，BERT[3]旨在恢复遮蔽词;在CV (Computer Vision)中，MAE[2]恢复图像的像素点（块）。对于图，GAE (Graph Autoencoder)重建图的结构信息或节点特征。现有的图数据动编码器大多着眼于链接预测和图数据...
Graph Auto-Encoders (GAEs) are end-to-end trainable neural network models for unsupervised learning, clustering and link prediction on graphs. GAEs have successfully been used for: Link prediction in large-scale relational data: M. Schlichtkrull & T. N. Kipf et al.,Modeling Relational Data ...
Multi-level Graph Autoencoder (GAE) to clarify cell cell interactions and gene regulatory network inference from spatially resolved transcriptomics gene-regulatory-networkgraph-auto-encodercell-cell-interaction UpdatedJan 9, 2025 Jupyter Notebook
Network Representations with Adversarially Regularized Autoencoders (NetRA) Deep Neural Networks for Graph Representations (DNGR) Structural Deep Network Embedding (SDNE) Deep Recursive Network Embedding (DRNE) DNGR和SDNE学习仅给出拓扑结构的节点嵌入,而GAE、ARGA、NetRA、DRNE用于学习当拓扑信息和节点内容特...
Graph Autoencoder (GAE)和Adversarially Regularized Graph Autoencoder (ARGA) 图自编码器的其它变体有: 具有反向正则化自动编码器的网络表示Network Representations with Adversarially Regularized Autoencoders (NetRA) 用于图表示的深度神经网络Deep Neural Networks for Graph Representations (DNGR) ...