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Temporal Graph Benchmark With Link Anomaly Detection Task

Overview

This repository contains code for link anomaly detection task, integrated into Temporal Graph Benchmark (TGB). The code is based on TGB repository.

Install Dependencies

Our implementation works with python >= 3.9 and can be installed as follows:

1. Set up a conda environment.

conda create -n tgb_env python=3.9
conda activate tgb_env

2. Install external packages.

pip install pandas==1.5.3
pip install matplotlib==3.7.1
pip install clint==0.5.1

Install Pytorch and PyG dependencies to run the examples.

pip install torch==2.0.0 --index-url https://download.pytorch.org/whl/cu117
pip install torch_geometric==2.3.0
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cu117.html

3. Install local dependencies under the root directory /TGB-link-anomaly-detection.

pip install -e .

Generating Anomalies

The code for generation of anomalies can be found in /tgb/datasets/dataset_scripts/link_anomaly_generator.py.

For example, to generate temporal-structural-contextual anomalies for the Wikipedia dataset from TGB, run the following command inside /tgb/datasets/dataset_scripts directory:

python link_anomaly_generator.py  \
--dataset_name tgbl-wiki  \
--val_ratio 0.15  \
--test_ratio 0.15  \
--anom_type temporal-structural-contextual  \
--output_root <OUTPUT-DIR>

The anomalies are generated for the validation and test splits according to the 70/15/15 data split. The data is saved under <OUTPUT-DIR> directory, which should be specified as an absolute path.

Running Example Methods

• For the link anomaly detection task, see the examples/linkanomdet directory for an example script to run TGN model on a TGB dataset. Note that the example requires generated anomalies, which can be obtained by running the example command in Generating Anomalies section.
• For all other baselines, please see the CTDG-link-anomaly-detection repository.

Citation

If this repository is helpful for your research, please consider citing our paper below.

@article{postuvan2024learningbased,
  title={Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs},
  author={Tim Postuvan and Claas Grohnfeldt and Michele Russo and Giulio Lovisotto},
  journal={Transactions on Machine Learning Research},
  issn={2835-8856},
  year={2024},
  url={https://openreview.net/forum?id=8imVCizVEw}
}