Name
Title | ||
|---|---|---|
Probabilistic Graph Convolutional Network via Topology-Constrained Latent Space Model |
Abstract | ||
|---|---|---|
Although many graph convolutional neural networks (GCNNs) have achieved superior performances in semisupervised node classification, they are designed from either the spatial or spectral perspective, yet without a general theoretical basis. Besides, most of the existing GCNNs methods tend to ignore the ubiquitous noises in the network topology and node content and are thus unable to model t... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCYB.2020.3005938 | IEEE Transactions on Cybernetics |
Keywords | DocType | Volume |
Network topology,Probabilistic logic,Convolution,Uncertainty,Gaussian distribution,Data models,Laplace equations | Journal | 52 |
Issue | ISSN | Citations |
4 | 2168-2267 | 1 |
PageRank | References | Authors |
0.35 | 23 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Yang | 1 | 213 | 16.53 |
| Yuanfang Guo | 2 | 95 | 18.21 |
| Junhua Gu | 3 | 1 | 0.35 |
| Di Jin | 4 | 317 | 49.25 |
| Bo Yang | 5 | 822 | 64.08 |
| Xiaochun Cao | 6 | 1986 | 131.55 |