Supplementary code for the work published at CIKM 2023.
Paper link: https://dl.acm.org/doi/10.1145/3583780.3615032
Arxiv: https://arxiv.org/abs/2207.03408
@inproceedings{sharma2023representation,
title={Representation Learning in Continuous-Time Dynamic Signed Networks},
author={Sharma, Kartik and Raghavendra, Mohit and Lee, Yeon-Chang and Kumar M, Anand and Kumar, Srijan},
booktitle={32nd ACM International Conference on Information and Knowledge Management},
year={2023},
url={https://doi.org/10.1145/3583780.3615032}\ }
We use Python 3.8 for this implementation. Our code extensively uses the following 3 libraries:
Follow the steps in the given links to install these libraries for your system configuration.
A full set of requirements is given in requirements.txt, which can be used to create a conda virtual environment as:
conda create --name <env> --file requirements.txt
We use these 4 datasets in this work:
These will be automatically downloaded and processed inside the root directory data/.
python train.py --model \<MODEL> --dataset \<DATASET> --task \<TASK>
where we allow the following possibilities
| MODEL | gcn, gat, sgcn, sigat, tgn, tgat, caw, sgclstm, semba |
| DATASET | BitcoinOTC-1, BitcoinAlpha-1, epinions, wikirfa |
| TASK | signlink_class, sign_class, link_pred |
Example run: ./run.sh
In addition, we can tune other hyperparameters such as number of epochs, initial learning rate, embedding dimensions, negative weight, and so on. We provide the best run details of some of these in best_run_details.csv while we use the default parameters for others.
Note that for Epinions dataset, pass the batch size as --batch_size 16000.
Running all methods: ./run_baselines.sh
Saved models: We also provide saved models for each model, dataset, and task triplet except for Epinions. Test performance can be found by simply running python eval.py --model \<MODEL> --dataset \<DATASET> --task \<TASK>. All saved models are provided at https://drive.google.com/file/d/1LEtIYwY67NlJsmMXkepOX6fstO8HA8vM/view?usp=drive_link