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OpenAlex Citation Counts

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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:

Alleviating Structural Distribution Shift in Graph Anomaly Detection
Yuan Gao, Xiang Wang, Xiangnan He, et al.
(2023)
Open Access | Times Cited: 25

Showing 25 citing articles:

Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum
Fan Xu, Nan Wang, Hao Wu, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2024) Vol. 38, Iss. 8, pp. 9214-9222
Open Access | Times Cited: 8
Causal invariant geographic network representations with feature and structural distribution shifts
Yuhan Wang, Silu He, Qinyao Luo, et al.
Future Generation Computer Systems (2025), pp. 107814-107814
Closed Access
Graph anomaly detection based on hybrid node representation learning
Xiang Wang, Hao Dou, Dibo Dong, et al.
Neural Networks (2025) Vol. 185, pp. 107169-107169
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
Fraud Detection in E-Commerce: A Systematic Review of Transaction Risk Prevention
Susie Xi Rao, Jiawei Jiang, Zhichao Han, et al.
IntechOpen eBooks (2025)
Closed Access
Beyond the individual: An improved telecom fraud detection approach based on latent synergy graph learning
Junhang Wu, Ruimin Hu, Dengshi Li, et al.
Neural Networks (2023) Vol. 169, pp. 20-31
Open Access | Times Cited: 7
PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization
Ziqi Yuan, Haoyi Zhou, Tianyu Chen, et al.
(2024), pp. 920-929
Open Access | Times Cited: 2
Invariant Graph Learning for Causal Effect Estimation
Yongduo Sui, Caizhi Tang, Zhixuan Chu, et al.
Proceedings of the ACM Web Conference 2022 (2024), pp. 2552-2562
Closed Access | Times Cited: 2
EXGC: Bridging Efficiency and Explainability in Graph Condensation
Junfeng Fang, Xinglin Li, Yongduo Sui, et al.
Proceedings of the ACM Web Conference 2022 (2024), pp. 721-732
Open Access | Times Cited: 2
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: 2
WAKE: A Weakly Supervised Business Process Anomaly Detection Framework via a Pre-Trained Autoencoder
Wei Guan, Jian Cao, Haiyan Zhao, et al.
IEEE Transactions on Knowledge and Data Engineering (2023) Vol. 36, Iss. 6, pp. 2745-2758
Closed Access | Times Cited: 5
Graph Anomaly Detection with Bi-level Optimization
Yuan Gao, Junfeng Fang, Yongduo Sui, et al.
Proceedings of the ACM Web Conference 2022 (2024), pp. 4383-4394
Closed Access | Times Cited: 1
Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation
Wentao Shi, Chenxu Wang, Fuli Feng, et al.
Proceedings of the ACM Web Conference 2022 (2024), pp. 3253-3264
Open Access
Revisiting Attack-caused Structural Distribution Shift in Graph Anomaly Detection
Yuan Gao, Jinghan Li, Xiang Wang, et al.
IEEE Transactions on Knowledge and Data Engineering (2024) Vol. 36, Iss. 9, pp. 4849-4861
Closed Access
Do not ignore heterogeneity and heterophily: Multi-network collaborative telecom fraud detection
Lingfei Ren, Yilong Zang, Ruimin Hu, et al.
Expert Systems with Applications (2024) Vol. 257, pp. 124974-124974
Closed Access
Graph Anomaly Detection with Few Labels: A Data-Centric Approach
Xiaoxiao Ma, Ruikun Li, Fanzhen Liu, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2024), pp. 2153-2164
Open Access
A Structural Information Guided Hierarchical Reconstruction for Graph Anomaly Detection
Dongcheng Zou, Hao Peng, Chunyang Liu
(2024), pp. 4318-4323
Closed Access
GAD: A Generalized Framework for Anomaly Detection at Different Risk Levels
Ronglei Wei, Zhu‐Qing He, Martin Pavlovski, et al.
(2024), pp. 2513-2522
Closed Access
Graph Local Homophily Network for Anomaly Detection
Ronghui Guo, Minghui Zou, Sai Zhang, et al.
(2024), pp. 706-716
Open Access
Heterophilic Graph Invariant Learning for Out-of-Distribution of Fraud Detection
Lingfei Ren, Ruimin Hu, Zheng Wang, et al.
(2024), pp. 11032-11040
Closed Access
Towards Effective Federated Graph Anomaly Detection via Self-boosted Knowledge Distillation
Jinyu Cai, Yunhe Zhang, Zhoumin Lu, et al.
(2024), pp. 5537-5546
Closed Access
FCMH: Fast Cluster Multi-hop Model for Graph Fraud Detection
Rui Zhang, Wenbo Li, Xiaodong Ning, et al.
Lecture notes in computer science (2024), pp. 34-49
Closed Access
Deep Learning for Anomaly Detection in Time-Series Data: An Analysis of Techniques, Review of Applications, and Guidelines for Future Research
Usman Ahmad Usmani, Izzatdin Abdul Aziz, Jafreezal Jaafar, et al.
IEEE Access (2024) Vol. 12, pp. 174564-174590
Open Access
Dynamic Relation-Attentive Graph Neural Networks for Fraud Detection
Heehyeon Kim, Jinhyeok Choi, Joyce Jiyoung Whang
2022 IEEE International Conference on Data Mining Workshops (ICDMW) (2023)
Open Access | Times Cited: 1
An anomaly aware network embedding framework for unsupervised anomalous link detection
Dongsheng Duan, Cheng Zhang, Lingling Tong, et al.
Data Mining and Knowledge Discovery (2023) Vol. 38, Iss. 2, pp. 501-534
Closed Access
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