Next Article in Journal
An Improved YOLOv8n Framework for PCB Defect Detection via C2f-Mamba Feature Extraction and FPN-PAN++ Multi-Scale Fusion
Previous Article in Journal
Synchronization Analysis for a Class of Proportional Caputo Fractional-Order Neural Networks
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Symmetry
Volume 18
Issue 6
10.3390/sym18060968
symmetry-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Global-Local Complementary Fusion: Unsupervised Graph Anomaly Detection via Diffusion Reconstruction and Contrastive Learning

by
Ruibin Hu
1,
Qian Chen
2,3,
Huiying Xu
2,3,*,
Ruidong Wang
4,
Huazhen Jin
5,*,
Xiao Huang
4 and
Xinzhong Zhu
2,3
1
Science and Technology Department, Zhejiang Normal University, Jinhua 321004, China
2
Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China
3
College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
4
University of Kansas Joint College of Education, Zhejiang Normal University, Jinhua 321004, China
5
Jinhua Audit Bureau, Jinhua 321000, China
*
Authors to whom correspondence should be addressed.
Symmetry 2026, 18(6), 968; https://doi.org/10.3390/sym18060968 (registering DOI)
Submission received: 28 April 2026 / Revised: 24 May 2026 / Accepted: 1 June 2026 / Published: 3 June 2026
(This article belongs to the Section Computer)
Versions Notes

Abstract

Anomaly detection on attributed graphs is essential for scientific integrity, cybersecurity, and financial oversight, where abnormal patterns often manifest as breaks in structure or attributes. However, existing unsupervised methods are difficult to combine both global and local perspectives to detect anomalies. To address this issue, we propose DCGAD, a unified unsupervised framework that captures anomalies by fusing global reconstruction error and local view inconsistency. Our model leverages diffusion reconstruction to strengthen global semantic information, employing two parallel autoencoders to reconstruct the graph structure based on the original features and diffusion-enhanced features, respectively, to capture global structural differences. Complementarily, the model samples two local subgraph views per target node and uses multi-view contrastive learning to evaluate local contextual inconsistencies. By jointly optimizing these two complementary objectives, our proposed model achieves collaborative use of local and global information. Extensive experiments on six real-world graph datasets show that DCGAD outperforms other state-of-the-art approaches, achieving excellent scores on citation networks and significant gains on social and collaborative platforms.
Keywords: anomaly detection; graph neural networks; contrastive learning; graph diffusion; attributed graph anomaly detection; graph neural networks; contrastive learning; graph diffusion; attributed graph

Share and Cite

MDPI and ACS Style

Hu, R.; Chen, Q.; Xu, H.; Wang, R.; Jin, H.; Huang, X.; Zhu, X. Global-Local Complementary Fusion: Unsupervised Graph Anomaly Detection via Diffusion Reconstruction and Contrastive Learning. Symmetry 2026, 18, 968. https://doi.org/10.3390/sym18060968

AMA Style

Hu R, Chen Q, Xu H, Wang R, Jin H, Huang X, Zhu X. Global-Local Complementary Fusion: Unsupervised Graph Anomaly Detection via Diffusion Reconstruction and Contrastive Learning. Symmetry. 2026; 18(6):968. https://doi.org/10.3390/sym18060968

Chicago/Turabian Style

Hu, Ruibin, Qian Chen, Huiying Xu, Ruidong Wang, Huazhen Jin, Xiao Huang, and Xinzhong Zhu. 2026. "Global-Local Complementary Fusion: Unsupervised Graph Anomaly Detection via Diffusion Reconstruction and Contrastive Learning" Symmetry 18, no. 6: 968. https://doi.org/10.3390/sym18060968

APA Style

Hu, R., Chen, Q., Xu, H., Wang, R., Jin, H., Huang, X., & Zhu, X. (2026). Global-Local Complementary Fusion: Unsupervised Graph Anomaly Detection via Diffusion Reconstruction and Contrastive Learning. Symmetry, 18(6), 968. https://doi.org/10.3390/sym18060968

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop