Graph Models
Cluster-GCN
Introduced by Chiang et al. in Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional NetworksCluster-GCN is a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure. Cluster-GCN works as the following: at each step, it samples a block of nodes that associate with a dense subgraph identified by a graph clustering algorithm, and restricts the neighborhood search within this subgraph. This simple but effective strategy leads to significantly improved memory and computational efficiency while being able to achieve comparable test accuracy with previous algorithms.
Description and image from: Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Source: Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Graph Learning | 2 | 16.67% |
| Node Classification | 2 | 16.67% |
| Clustering | 2 | 16.67% |
| whole slide images | 1 | 8.33% |
| Face Clustering | 1 | 8.33% |
| imbalanced classification | 1 | 8.33% |
| Computational Efficiency | 1 | 8.33% |
| Graph Clustering | 1 | 8.33% |
| Link Prediction | 1 | 8.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
