Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Graph Anomaly Detection With Graph Neural Networks: Current Status and Challenges
Hwan Kim, Byung Suk Lee, Won-Yong Shin, et al.
IEEE Access (2022) Vol. 10, pp. 111820-111829
Open Access | Times Cited: 59

Showing 1-25 of 59 citing articles:

AI-driven fusion with cybersecurity: Exploring current trends, advanced techniques, future directions, and policy implications for evolving paradigms– A comprehensive review
Sijjad Ali, Jia Wang, Victor Chung Ming Leung
Information Fusion (2025), pp. 102922-102922
Closed Access | Times Cited: 2
Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum
Yuan Gao, Xiang Wang, Xiangnan He, et al.
Proceedings of the ACM Web Conference 2022 (2023)
Open Access | Times Cited: 39
Financial fraud detection using graph neural networks: A systematic review
Soroor Motie, Bijan Raahemi
Expert Systems with Applications (2023) Vol. 240, pp. 122156-122156
Closed Access | Times Cited: 28
Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection
Zhiyu Ren, Xiaojie Li, Jing Peng, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 7
Graph Neural Network for Spatiotemporal Data: Methods and Applications
Yun Li, Dazhou Yu, Zhenke Liu, et al.
(2024)
Open Access | Times Cited: 6
Process-Oriented heterogeneous graph learning in GNN-Based ICS anomalous pattern recognition
Shuaiyi Lu, Kai Wang, Liren Zhang, et al.
Pattern Recognition (2023) Vol. 141, pp. 109661-109661
Closed Access | Times Cited: 13
Deep anomaly detection on set data: Survey and comparison
Michaela Mašková, Matěj Zorek, Tomáš Pevný, et al.
Pattern Recognition (2024) Vol. 151, pp. 110381-110381
Closed Access | Times Cited: 4
Anomaly Detection in Dynamic Graphs: A Comprehensive Survey
Ocheme Anthony Ekle, William Eberle
ACM Transactions on Knowledge Discovery from Data (2024) Vol. 18, Iss. 8, pp. 1-44
Open Access | Times Cited: 4
A novel approach for graph-based real-time anomaly detection from dynamic network data listened by Wireshark
Muhammet Onur Kaya, Mehmet Özdem, Resul Daş
EAI Endorsed Transactions on Industrial Networks and Intelligent Systems (2025) Vol. 12, Iss. 2
Open Access
Detecting malicious IoT network communication through Graph Neural Networks in real-world conditions
Vincenzo Carletti, Pasquale Foggia, Francesco Rosa, et al.
Pattern Recognition Letters (2025)
Closed Access
A novel hybrid approach combining GCN and GAT for effective anomaly detection from firewall logs in campus networks
A. R. Yilmaz, Resul Daş
Computer Networks (2025), pp. 111082-111082
Closed Access
A comprehensive survey on GNN-based anomaly detection: taxonomy, methods, and the role of large language models
Ziqi Yuan, Qingyun Sun, Haoyi Zhou, et al.
International Journal of Machine Learning and Cybernetics (2025)
Closed Access
A Survey to Time Series Anomaly Detection in Different Data Structures
Yinglun Dong, Chuanlei Zhang, Bing Zhen, et al.
Communications in computer and information science (2025), pp. 206-217
Closed Access
LM-Hunter: An NLP-Powered Graph Method for Detecting Adversary Lateral Movements in APT Cyber-Attacks at Scale
Mario Pérez-Gomariz, Fernando Cerdán, Jess García
Computer Networks (2025), pp. 111181-111181
Closed Access
Is the real-time data of process safety reliable? An anomaly detection method based on the Graph Neural Network
Jianrong Zhang, Wei Zhang, Wenbin Liao, et al.
Process Safety and Environmental Protection (2025), pp. 107066-107066
Closed Access
Multi-view graph anomaly detection via subgraph anomaly augmentation
Fu Lin, Yue Zhang, Xuexiong Luo, et al.
Neurocomputing (2025), pp. 130109-130109
Closed Access
Modified GANs Based on GNN for Anomaly Detection in Graphs
Premanand Ghadekar, Ravjibhai Karshanbhai Chaudhari, Kshitij Bisen, et al.
Lecture notes on data engineering and communications technologies (2025), pp. 319-330
Closed Access
Anomaly Detection Module for Network Traffic Monitoring in Public Institutions
Łukasz Wawrowski, A. Białas, Adrian Kajzer, et al.
Sensors (2023) Vol. 23, Iss. 6, pp. 2974-2974
Open Access | Times Cited: 10
TA-Detector: A GNN-based Anomaly Detector via Trust Relationship
Jie Wen, Nan Jiang, Lang Li, et al.
ACM Transactions on Multimedia Computing Communications and Applications (2024)
Open Access | Times Cited: 3
Unsupervised graph anomaly detection with discriminative embedding similarity for viscoelastic sandwich cylindrical structures
Rujie Hou, Zhousuo Zhang, Jinglong Chen, et al.
ISA Transactions (2024) Vol. 147, pp. 36-54
Closed Access | Times Cited: 2
Drug repurposing based on the DTD-GNN graph neural network: revealing the relationships among drugs, targets and diseases
Wenjun Li, Wanjun Ma, Mengyun Yang, et al.
BMC Genomics (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 2
Anomaly traffic detection in IoT security using graph neural networks
Mengnan Gao, Lifa Wu, Qi Li, et al.
Journal of Information Security and Applications (2023) Vol. 76, pp. 103532-103532
Closed Access | Times Cited: 5
Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurement
Lucas Potin, Rosa Figueiredo, Vincent Labatut, et al.
Lecture notes in computer science (2023), pp. 69-87
Open Access | Times Cited: 4
Detecting Anomalies in Water Quality Monitoring Using Deep Learning
Sarafudheen M. Tharayil, Nada K. Alomari, Dana K. Bubshait
(2024)
Closed Access | Times Cited: 1
Enhanced patient-based real-time quality control using the graph-based anomaly detection
Xueling Shang, Minglong Zhang, Dehui Sun, et al.
Clinical Chemistry and Laboratory Medicine (CCLM) (2024) Vol. 62, Iss. 12, pp. 2451-2460
Closed Access | Times Cited: 1
Page 1 - Next Page

Scroll to top