dmlc--dgl
9c790b1143
* convert np.ndarray to backend tensor * add datasets * add qm7 * add dataset * add dataset * fix * change ppi * tu dataset * add datasets * fix * fix * fix * fix * add docstring * docs * doc
Hierarchical Graph Representation Learning with Differentiable Pooling
Paper link: https://arxiv.org/abs/1806.08804
Author's code repo: https://github.com/RexYing/diffpool
This folder contains a DGL implementation of the DiffPool model. The first pooling layer is computed with DGL, and following pooling layers are computed with tensorized operation since the pooled graphs are dense.
Dependencies
- PyTorch 1.0+
How to run
python train.py --dataset ENZYMES --pool_ratio 0.10 --num_pool 1
python train.py --dataset DD --pool_ratio 0.15 --num_pool 1
Performance
ENZYMES 63.33% (with early stopping) DD 79.31% (with early stopping)