Graph Models
DiffPool
Introduced by Ying et al. in Hierarchical Graph Representation Learning with Differentiable PoolingDiffPool is a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. DiffPool learns a differentiable soft cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a set of clusters, which then form the coarsened input for the next GNN layer.
Description and image from: Hierarchical Graph Representation Learning with Differentiable Pooling
Source: Hierarchical Graph Representation Learning with Differentiable Pooling
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Graph Classification | 3 | 20.00% |
| General Classification | 2 | 13.33% |
| Active Learning | 1 | 6.67% |
| Molecular Property Prediction | 1 | 6.67% |
| Property Prediction | 1 | 6.67% |
| Time Series Analysis | 1 | 6.67% |
| Graph Attention | 1 | 6.67% |
| graph partitioning | 1 | 6.67% |
| Classification | 1 | 6.67% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
