Awesome

NWR-GAE

An implementation of ICLR 2022 Paper: [Graph Auto-Encoder via Neighborhood Wasserstein Reconstruction] (https://openreview.net/forum?id=ATUh28lnSuW)

Before to execute NWR-GAE, it is necessary to install the following packages using requirments.txt:

pip install -r requirements.txt

The repository is organised as follows:

data/ contains the necessary python files for generating synthetic data;
datasets/ contains the necessary dataset files for real-world datasets;
edgelists/ contains the necessary dataset files for real-world datasets in edgelist format;
src/ contains the implementation of the NW-GAE pipeline (model.py) and our training file (train.py);
src/layers contains the network layers, such as MLP, PairNorm (layers.py).
src/utils contains the necessary processing subroutines (utils.py).

Proximity: cora, citeseer, pubmed

Structure: cornell, texas, wisconsin

Mixed: chameleon, squirrel, film (actor)

Synthetic: generated from https://github.com/snap-stanford/graphwave

--dataset, supported datasets above, default: texas

--lr, learning rate for neighborhood reconstructor, default: 5e-5

--epoch_num, training epoch size, default: 100

--lambda_loss1, balance weights for degree and neighborhood information decoder, default: 1e-2

--lambda_loss2, balance weights for feature decoder, default: 1e-2

--sample_size, size of neighborhood down sampling, default: 5

--dimension, dimension of final output embeddings, default: 1500

cd src
python train.py --dataset texas # real-world datasets
python train.py --dataset_type synthetic # Synthetic datasets

, the default setting can run most of the state-of-art results (especially on structure-oriented/mixed datasets, i.e. cornell, texas, wisconsin).