Graph Models
GCNII
Introduced by Chen et al. in Simple and Deep Graph Convolutional NetworksGCNII is an extension of a Graph Convolution Networks with two new techniques, initial residual and identify mapping, to tackle the problem of oversmoothing -- where stacking more layers and adding non-linearity tends to degrade performance. At each layer, initial residual constructs a skip connection from the input layer, while identity mapping adds an identity matrix to the weight matrix.
Source: Simple and Deep Graph Convolutional Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Node Classification | 5 | 35.71% |
| Link Prediction | 2 | 14.29% |
| Classification | 1 | 7.14% |
| Graph Attention | 1 | 7.14% |
| Link Sign Prediction | 1 | 7.14% |
| Generalization Bounds | 1 | 7.14% |
| Graph Classification | 1 | 7.14% |
| Graph Regression | 1 | 7.14% |
| Node Classification on Non-Homophilic (Heterophilic) Graphs | 1 | 7.14% |
Residual Connection
