Modularity-aware graph autoencoders for joint community detection and link prediction
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as
powerful methods for link prediction. Their performances are less impressive on community …
powerful methods for link prediction. Their performances are less impressive on community …
MTGCN: A multi-task approach for node classification and link prediction in graph data
Both node classification and link prediction are popular topics of supervised learning on the
graph data, but previous works seldom integrate them together to capture their …
graph data, but previous works seldom integrate them together to capture their …
Simple and effective graph autoencoders with one-hop linear models
Over the last few years, graph autoencoders (AE) and variational autoencoders (VAE)
emerged as powerful node embedding methods, with promising performances on …
emerged as powerful node embedding methods, with promising performances on …
Functions predict horizontal gene transfer and the emergence of antibiotic resistance
Phylogenetic distance, shared ecology, and genomic constraints are often cited as key
drivers governing horizontal gene transfer (HGT), although their relative contributions are …
drivers governing horizontal gene transfer (HGT), although their relative contributions are …
Keep it simple: Graph autoencoders without graph convolutional networks
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful
node embedding methods, with promising performances on challenging tasks such as link …
node embedding methods, with promising performances on challenging tasks such as link …
Multitask representation learning with multiview graph convolutional networks
Link prediction and node classification are two important downstream tasks of network
representation learning. Existing methods have achieved acceptable results but they …
representation learning. Existing methods have achieved acceptable results but they …
Graph representation learning based on deep generative gaussian mixture models
Graph representation learning is an effective tool for facilitating graph analysis with machine
learning methods. Most GNNs, including Graph Convolutional Networks (GCN), Graph …
learning methods. Most GNNs, including Graph Convolutional Networks (GCN), Graph …
Enhancing graph neural networks via auxiliary training for semi-supervised node classification
Abstract Graph Neural Networks (GNNs) have been successfully applied to graph analysis
tasks. As a canonical task in graph analysis, node classification has achieved promising …
tasks. As a canonical task in graph analysis, node classification has achieved promising …
Graph star net for generalized multi-task learning
L Haonan, SH Huang, T Ye, G Xiuyan - arXiv preprint arXiv:1906.12330, 2019 - arxiv.org
In this work, we present graph star net (GraphStar), a novel and unified graph neural net
architecture which utilizes message-passing relay and attention mechanism for multiple …
architecture which utilizes message-passing relay and attention mechanism for multiple …
Fastgae: Scalable graph autoencoders with stochastic subgraph decoding
Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node
embedding methods, but suffer from scalability issues. In this paper, we introduce FastGAE …
embedding methods, but suffer from scalability issues. In this paper, we introduce FastGAE …