GNAR: graph contrastive learning networks with adaptive readouts for anomaly detection

Changcheng Wan; Suixiang Gao

doi:10.1117/12.3031986

8 June 2024 GNAR: graph contrastive learning networks with adaptive readouts for anomaly detection

Changcheng Wan, Suixiang Gao

Author Affiliations +

Proceedings Volume 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024); 1317128 (2024) https://doi.org/10.1117/12.3031986
Event: Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024), 2024, Jinan, China

Abstract

Recent advancements in graph neural networks (GNNs) have prompted diverse research endeavors focused on utilizing GNNs for anomaly detection. The fundamental concept revolves around harnessing the inherent expressive capabilities of GNNs to acquire meaningful node representations, aiming to distinguish between anomalous and normal nodes in the embedding space. However, prior methods have often employed simple readout modules (such as sum, mean, or max functions) for subgraph aggregation, failing to fully exploit subgraph information. In response to this limitation, we propose an anomaly detection application algorithm called “Graph Contrastive Learning Network with Adaptive Readouts” (GNAR), tailored specifically for Graph Anomaly Detection (GAD) tasks. Through extensive experiments on three famous public datasets, we consistently observe that GNAR outperforms baseline methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Changcheng Wan and Suixiang Gao "GNAR: graph contrastive learning networks with adaptive readouts for anomaly detection", Proc. SPIE 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024), 1317128 (8 June 2024); https://doi.org/10.1117/12.3031986

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE