TenGAN

Tensor Model

How to run

Run the model using Tensor_GAN.py:

python Tensor_GAN.py --dataset=football_small --n_epochs=5000 --rank=100 --version=1.0.0 --model_name=${MODEL_NAME_HERE} --gen_class=NewCPTensorGenerator --disc_class=NewLayeredMultiviewDiscriminator --sampling_method=random_walk

It uses Weights & Biases by default for logging. You can make a free account there or just log locally.

Installing

Install the requirements from requirements.txt using pip install -r requirements.txt. Unfortunately some requirements may be missing from the list (and may have to be installed as you go).

You will also have to install the gutils module from the local directory here. You can do this with the following commands:

$ cd gutils
$ python setup.py install

Datasets

We used 4 datasets in the paper. 3 of them are publicly available:

• football: Can be downloaded here. Place it in data/football.
• enron: Can be downloaded here. Extract and place it in data/enron/enron.tns. Then, you can run scripts/sample_enron.py to subsample from it.
• nell2: Can be downloaded here. Extract and place it in data/nell2/nell2.tns. Then, you can run scripts/sample_dense_nell.py to subsample from it.

Key parameters

• --rank: controls the parameter r in the model (e.g. generated matrix A will be nxr).
• --dataset: controls which dataset we use. Must correspond to the correct name in the create_tensor function of dsloader/tensor_creator.py.
• --tensor_slices: the number of views/slices in our graph/tensor. Depends on the dataset.

The other parameters can be seen in the opt dictionary in Tensor_GAN.py. Some may not be relevant to tensor generation and may be leftover artifacts from graph generation.

Baseline

The HGEN included here is a modified version of the original HGEN code.

hgen_converter converts a TenGAN dataset into a HGEN-compatible dataset. You can then run HGEN with the corresponding dataset name.

This also relies on the files in the hin2vec directory, which is a modified version of https://github.com/csiesheep/hin2vec with some bug fixes and updated to work with Python 3.

Adding a new dataset

Add a new elif block to create_tensor and the relevant loading logic.

The function to load the dataset should return a (np.array, list<nx.Graph>) tuple (you may be able to get it to work with only one, but may also require a little modification). See load_mtx as an example.

Then, you can set the dataset and tensor_slices parameters when you run the model, and it should use the corresponding dataset. Note that you may have to append _small or _large to the dataset depending on if you want smaller or larger samples.

Citation

If you use this code in your work, please cite our workshop paper (full version under review):

@article{shiao2023adversarial,
title={TenGAN: Adversarially Generating Multiplex Tensor Graphs},
author={Shiao, William and Miller, Benjamin A and Chan, Kevin and Yu, Paul and Eliassi-Rad, Tina and Papalexakis, Evangelos E},
year={2023},
journal={Data Mining and Knowledge Discovery},
publisher={Springer}
}