DEFT

This repository contains the code for Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactions, published in AAAI 2023.

Data

8 datasets were used in the paper:

• stochastic block model: Downloadable from https://github.com/IBM/EvolveGCN/tree/master/data
• bitcoin OTC: Downloadable from http://snap.stanford.edu/data/soc-sign-bitcoin-otc.html
• bitcoin Alpha: Downloadable from http://snap.stanford.edu/data/soc-sign-bitcoin-alpha.html
• uc_irvine: Downloadable from http://konect.uni-koblenz.de/networks/opsahl-ucsocial
• autonomous systems: Downloadable from http://snap.stanford.edu/data/as-733.html
• reddit hyperlink network: Downloadable from http://snap.stanford.edu/data/soc-RedditHyperlinks.html
• elliptic: Please see the instruction to manually prepare the preprocessed version or refer to the following repository that originally proposed the usage of the data: https://arxiv.org/abs/1902.10191
• brain: Downloadable from https://www.dropbox.com/sh/33p0gk4etgdjfvz/AACe2INXtp3N0u9xRdszq4vua?dl=0

For downloaded data sets please place them in the 'data' folder.

Requirements

• PyTorch 1.0 or higher
• Python 3.6

GPU availability is recommended to train the models. Otherwise, set the use_cuda flag in parameters.yaml to false.

Requirements

• install nvidia drivers

Usage

Set --config_file with a yaml configuration file to run the experiments. For example:

python run_exp.py --config_file ./experiments/parameters_example.yaml

Most of the parameters in the yaml configuration file are self-explanatory. The 'experiments' folder contains config file for the results reported in the DEFT paper.

Setting 'use_logfile' to True in the configuration yaml will output a file, in the 'log' directory, containing information about the experiment and validation metrics for the various epochs. The file could be manually analyzed, alternatively 'log_analyzer.py' can be used to automatically parse a log file and to retrieve the evaluation metrics at the best validation epoch. For example:

python log_analyzer.py log/filename.log

Reference

[1] Anson Bastos, Abhishek Nadgeri, Kuldeep Singh, Toyotaro Suzumura, Manish Singh. Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactions. AAAI 2023.

BibTeX entry

If you use our work kindly consider citing:

@misc{https://doi.org/10.48550/arxiv.2211.11979,
  doi = {10.48550/ARXIV.2211.11979},
  url = {https://arxiv.org/abs/2211.11979},
  author = {Bastos, Anson and Nadgeri, Abhishek and Singh, Kuldeep and Suzumura, Toyotaro and Singh, Manish},
  keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactions},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

Acknowledgements

This code has been adapted from EvolveGCN. Many thanks to the authors for sharing the code.