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RegraphGAN: A graph generative adversarial network model for dynamic network anomaly detection.

Dezhi Guo¹, Zhaowei Liu¹, Ranran Li¹

¹School of Computer and Control Engineering, Yantai University, Shandong, China.

Neural Networks : the Official Journal of the International Neural Network Society

|August 2, 2023

Summary

This summary is machine-generated.

This study introduces RegraphGAN, a novel graph generative adversarial network, enhancing dynamic graph anomaly detection efficiency and stability. The proposed method combines RegraphGAN with spatiotemporal coding for superior performance on real-world datasets.

Keywords:

Anomaly detection Dynamic networks Generative adversarial network

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Area of Science:

Artificial Intelligence
Data Science
Network Security

Background:

Dynamic graph anomaly detection is crucial in cybersecurity, social networks, and e-commerce.
Existing graph generative adversarial networks (GANs) lack efficient reverse mapping and struggle with complex dynamic graph data.

Purpose of the Study:

To propose a novel graph generative adversarial network (RegraphGAN) for improved dynamic graph anomaly detection.
To enhance training efficiency and stability of GAN models for dynamic graphs.
To address challenges in coding attribute-free node information in dynamic networks.

Main Methods:

Introduced RegraphGAN by incorporating encoders for mapping real data to latent space.
Combined RegraphGAN with spatiotemporal coding for dynamic network anomaly edge detection.
Conducted experiments on six real-world dynamic network datasets.

Main Results:

RegraphGAN demonstrated improved training efficiency and stability compared to original GANs.
The proposed method effectively handles complex dynamic graph data and attribute-free node information.
Anomaly detection experiments showed superior performance over existing methods.

Conclusions:

The novel RegraphGAN model significantly advances dynamic graph anomaly detection.
The integration with spatiotemporal coding offers a robust solution for complex network data.
The method shows strong potential for applications in cybersecurity and beyond.