Skip to content

Latest commit

 

History

History
109 lines (73 loc) · 5.5 KB

README.md

File metadata and controls

109 lines (73 loc) · 5.5 KB
Raw

Temporal Dynamics-Aware Adversarial Attacks on Discrete-Time Dynamic Graph Models

Code for the work published at KDD 2023

Citation

@inproceedings{sharma2023temporal,
title={Temporal Dynamics-Aware Adversarial Attacks on Discrete-Time Dynamic Graph Models},
author={Sharma, Kartik and Trivedi, Rakshit and Sridhar, Rohit and Kumar, Srijan},
booktitle={29th SIGKDD Conference on Knowledge Discovery and Data Mining - Research Track},
year={2023},
url={https://openreview.net/forum?id=Ox39VUFu-v}\ }

 

Requirements

We use Python 3.8 for this implementation. Our code extensively uses the following 3 libraries:

  1. PyTorch 1.7 (link)
  2. PyTorch Geometric (link)
  3. PyTorch Geometric Temporal (link)

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 --file requirements.txt

Data

We use these 4 datasets in this work:

  1. Radoslaw (link)
  2. UCI (link)
  3. Reddit hyperlinks (link)
  4. DBLP-5 (link)

To create a discrete-time dynamic graph with a given number of snapshots, use the following command inside ./data/:

python get_data.py --dataset <dataset_name> --num_graphs <num_snapshots>,

where dataset_name is the name of the directory inside which the dataset is stored in the data folder. We provide the pre-processed graphs for the 4 datasets inside ./data/.

Training victim models

We support training of 3 victim dynamic graph models - DySAT, EvolveGCN (both EvGCNO and EvGCNH), and GCLSTM. To train these models, go inside ./models/ and run the file train_models.sh.

DySAT is not provided by the torch_geometric_temporal library, so we use the available pytorch implementation. In order to train it, clone their repository inside models/ directory as:

git clone https://github.com/FeiGSSS/DySAT_pytorch

The description of the given arguments is given inside args.py. These arguments can be modified in order to train for different settings. In particular, one can change the field of dataset and model_name to train a specific dataset and model. Other parameters such as nepochs, learning_rate, and neg_weight can be tuned accordingly to train the model in a given setting. Note that the argument -dyn_feats must be passed when the dataset has dynamic features (like in DBLP), otherwise it must not be passed.

Baseline attack methods

Greedy

  1. Open run_greedy.sh and edit the fields according to the desired attack setting.
  2. Specify the model_name, dataset, task for attacking. Also, don't forget to pass dyn_feats if the dataset has dynamic features.
  3. Change the epsilons_arr to include the epsilon values that are to be tested on.
  4. Run ./run_greedy.sh.

Degree

  1. Open run_baselines.sh and edit the fields according to the desired attack setting.
  2. Change the method field to degree.
  3. Specify the model_name, dataset, task for attacking. Also, don't forget to pass dyn_feats if the dataset has dynamic features.
  4. Change the epsilons_arr to include the epsilon values that are to be tested on.
  5. Run ./run_baselines.sh.

Random

  1. Open run_baselines.sh and edit the fields according to the desired attack setting.
  2. Change the method field to degree.
  3. Specify the model_name, dataset, task for attacking. Also, don't forget to pass dyn_feats if the dataset has dynamic features.
  4. Change the epsilons_arr to include the epsilon values that are to be tested on.
  5. In case of feature perturbation, pass constraint as noise_feat instead of noise.
  6. Run ./run_baselines.sh.

TD-PGD

Attack

  1. Open run_pgd.sh and edit the fields according to the desired attack setting.
  2. Specify the model_name, dataset, task for attacking. Also, don't forget to pass dyn_feats if the dataset has dynamic features.
  3. Change the epsilons_arr to include the epsilon values that are to be tested on.
  4. In case of feature perturbation, set constraint as noise_feat instead of noise.
  5. In case of an online attack, set online as True.
  6. Run ./run_tdpgd.sh.

Anomaly Detection

Netwalk: Run run_attk_anomaly.sh for the required attack settings and find t-test between the scores stored in the pickle files using scikit-learn functions.

DynGem: After storing the attacks, run combine_results_noise_dzttest.py to obtain a t-test statistic and dz between dz'.

Analysis

  1. Open combine_results_noise.py.
  2. Choose the method from one of the following - pgd, greedy, degree, and random.
  3. Select constraint as noise for structural and noise_feat for feature perturbations.
  4. Make online to be True for online attacks.
  5. Run python combine_results_noise.py.

This will store the results inside the folder av_results/.

Visualization

Follow the notebook visualize.ipynb to make plots from a raw_results.tsv which can be formed by appending the results of different methods from av_results in one file.