ODIN

The repository is the implementation of ODIN: Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network Embedding, Hyunsik Yoo, Yeon-Chang Lee, Kijung Shin, Sang-Wook Kim, WWW '23: Proceedings of the ACM Web Conference 2023

Requirements

• python 37
• pytorch v1.13.0
• scikit-learn
• tqdm
• absl-py

pip install absl-py

• snap (Optional for "_data_preprocessing.py")

    python -m pip install snap-stanford (https://snap.stanford.edu/snappy/index.html)

Usage

You can use directly the following command to run the odin and save its learned embeddings in "--emb_file". Warning: you may want to specify the "input_file" and "--emb_file" on your own.

cd src
python app.py --embedding_size=20 --epochs=200 --option=odin --disen_weight=0.5 --input_file=../_Data/ciaodvd/noniid-in-barrier_1.0/u1/u1.edgelist --emb_file=../_Emb/ciaodvd/noniid-in-barrier_1.0/odin/u1_odin_200_4_odin_0.5_dim60.emb --neg_sample_rate=4

• embedding_size: the dimension of the embedding
• epochs: the number of epochs
• option: the option of the algorithm
• disen_weight: the weight of the disentanglement loss
• input_file: the input file
• emb_file: the embedding saving file
• neg_sample_rate: the number of negative samples

(Recommended) You can also use the "tester.py" and "_execute_methods.py" to run the odin with the user-specified hyperparameters, as well as to evaluate the embeddings via the link prediction tasks.

python tester.py

In "_tester.py", you can change the parameters of the ODIN and the datasets.

In "_Data/", all four non-iid-splitted datasets used in the paper are avaliable. If you want to reproduce the dataset from the raw dataset, "_data_preprocessing.py" can be used.

python _data_preprocessing.py