Anomaly Detection in Networks via Score-Based Generative Models

This is a repository for an ICML 2023 SPIGM Workshop paper and my Master's Thesis at Skoltech.

Authors: Dmitrii Gavrilev, Evgeny Burnaev (research advisor)

In this project, we use GDSS as a generative model.

Abstract

Node outlier detection in attributed graphs is a challenging problem for which there is no method that would work well across different datasets. Motivated by the state-of-the-art results of score-based models in graph generative modeling, we propose to incorporate them into the aforementioned problem. Our method achieves competitive results on small-scale graphs. We provide an empirical analysis of the Dirichlet energy, and show that generative models might struggle to accurately reconstruct it.

Prerequisites

• Install DGL and PyG
• pip install -r requirements.txt

Usage

python run_benchmark.py trains GDSS with random hyperparameters on a chosen dataset, runs inference with our methods, and repeats this pipeline 20 times. The result of inference is a .npy file with intermediate calculations.

Arguments:

• --config (path to a dataset config)
• --exp_name (the name of the experiment/checkpoints)
• --radius (the number of hops in ego-graphs)
• --trajectory_sample (the number of samples per trajectory; $K$ in the paper)
• --num_sample (the number of samples per node; $S$ in the paper)
• --num_steps (the number of steps to denoise for the full time horizon $[0,1]$)
• --is_energy (if True, it will use shift in energy as a graph dissimilary)
• --skip_training (inference-only mode; assumes the checkpoints already exist)

We evaluate our methods in a notebook by processing intermediate calculations from .npy files. See an example of training, inference and evaluation in Colab:

(Matrix distance as a dissimilarity measure)

(Shift in energy as a dissimilarity measure)

Checkpoints

Optionally, you can download the model checkpoints here.

Unzip them at ./checkpoint/{dataset_name}/ and run the benchmark with the --skip_training True option.