Sep 4, 2021 � In this paper, we focus on link prediction in directed graphs and propose an end-to-end model called directed graph variational auto-encoder(�...
In this paper, we focus on link prediction in directed graphs and propose an end-to-end model called directed graph variational auto-encoder(DGVAE).
Scalable Graph Convolutional Networks With Fast Localized Spectral Filter for Directed Graphs � Jun 2020.
本文主要研究了有向图中的链路预测问题，提出了一种端到端模型——有向图变分自编码器(DGVAE)。DGVAE以变分自编码器(VAE)为整体框架，采用两层GNN模型FDGCN作为编码器。我们�...
DGVAE is an end-to-end trainable neural network model for unsupervised learning, generation and clustering on graphs.
Missing: Link Prediction Directed
May 23, 2019 � We present a new gravity-inspired decoder scheme that can effectively reconstruct directed graphs from a node embedding.
Missing: DGVAE: End- end
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Apr 25, 2024 � DGVAE: An End-to-end Model for Link Prediction in Directed Graphs. ICIAI 2021: 202-207; 2020. [j5]. view. electronic edition via DOI (open�...
Sep 20, 2024 � In addition, various tasks that. GNNs can tackle, including node classification, graph classification, link prediction, graph generation and�...
In this work, we present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors. 1.
DGVAE:An End-to-end Model for Link Prediction in Directed Graphs. Conference Paper. Mar 2021. Chensheng Li � Xiaowei Qin�...