dmlc--dgl
HQ
684a61ad09
* diffpool original file added * make diffpool fuse up and running * minor tweak on tu dataset statistics method * fix tu * break * delete break * pre_org * diffpool fuse reorg * fix random shuffling * fix bn * add dgl layers * early stopping * add readme * fix * add diffpool preprocess script * tweak tu dataset * tweak * tweak * tweak * tweak * tweak * preprocess dataset * fix early stopping * fix * fix * fix * tweak * readme * code review * code review * dataset code review * update README * code review * tu doc
795 B
795 B
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)