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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:

AUC-oriented Graph Neural Network for Fraud Detection
Mengda Huang, Yang Liu, Xiang Ao, et al.
Proceedings of the ACM Web Conference 2022 (2022), pp. 1311-1321
Open Access | Times Cited: 50

Showing 1-25 of 50 citing articles:

AUC Maximization in the Era of Big Data and AI: A Survey
Tianbao Yang, Yiming Ying
ACM Computing Surveys (2022) Vol. 55, Iss. 8, pp. 1-37
Open Access | Times Cited: 142
GCCAD: Graph Contrastive Learning for Anomaly Detection
Bo Chen, Jing Zhang, Xiaokang Zhang, et al.
IEEE Transactions on Knowledge and Data Engineering (2022), pp. 1-14
Closed Access | Times Cited: 43
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: 38
LGM-GNN: A Local and Global Aware Memory-Based Graph Neural Network for Fraud Detection
Pengbo Li, Hang Yu, Xiangfeng Luo, et al.
IEEE Transactions on Big Data (2023) Vol. 9, Iss. 4, pp. 1116-1127
Closed Access | Times Cited: 30
Credit Card Fraud Detection via Intelligent Sampling and Self-supervised Learning
Chiao-Ting Chen, Chi Lee, Szu-Hao Huang, et al.
ACM Transactions on Intelligent Systems and Technology (2024) Vol. 15, Iss. 2, pp. 1-29
Closed Access | Times Cited: 7
Synthesizing global and local perspectives in contrastive learning for graph anomaly detection
Qiqi Yang, Hang Yu, Zhengyang Liu, et al.
Knowledge-Based Systems (2025), pp. 113289-113289
Closed Access
Reliable Representations Make A Stronger Defender
Kuan Li, Yang Liu, Xiang Ao, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022), pp. 925-935
Open Access | Times Cited: 26
Towards an Optimal Asymmetric Graph Structure for Robust Semi-supervised Node Classification
Zixing Song, Yifei Zhang, Irwin King
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022), pp. 1656-1665
Closed Access | Times Cited: 22
SCN_GNN: A GNN-based fraud detection algorithm combining strong node and graph topology information
Jing Chen, Quanzhen Chen, Feng Jiang, et al.
Expert Systems with Applications (2023) Vol. 237, pp. 121643-121643
Closed Access | Times Cited: 14
Incomplete Graph Learning via Attribute-Structure Decoupled Variational Auto-Encoder
Xinke Jiang, Zidi Qin, Jiarong Xu, et al.
(2024)
Open Access | Times Cited: 4
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
FLOOD: A Flexible Invariant Learning Framework for Out-of-Distribution Generalization on Graphs
Yang Liu, Xiang Ao, Fuli Feng, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2023), pp. 1548-1558
Open Access | Times Cited: 10
Temporal burstiness and collaborative camouflage aware fraud detection
Zheng Zhang, Jun Wan, Mingyang Zhou, et al.
Information Processing & Management (2022) Vol. 60, Iss. 2, pp. 103170-103170
Closed Access | Times Cited: 18
CLNode: Curriculum Learning for Node Classification
Xiaowen Wei, Xiuwen Gong, Yibing Zhan, et al.
(2023), pp. 670-678
Open Access | Times Cited: 9
Graph Learning for Fake Review Detection
Shuo Yu, Jing Ren, Shihao Li, et al.
Frontiers in Artificial Intelligence (2022) Vol. 5
Open Access | Times Cited: 15
Explainable Graph-based Fraud Detection via Neural Meta-graph Search
Zidi Qin, Yang Liu, Qing He, et al.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management (2022), pp. 4414-4418
Open Access | Times Cited: 14
LibAUC: A Deep Learning Library for X-Risk Optimization
Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2023), pp. 5487-5499
Open Access | Times Cited: 8
Next-Generation Building Condition Assessment: BIM and Neural Network Integration
Mani Amrouni Hosseini, Mehdi Ravanshadnia, Majid Rahimzadegan, et al.
Journal of Performance of Constructed Facilities (2024) Vol. 38, Iss. 6
Closed Access | Times Cited: 2
Beyond Homophily: Robust Graph Anomaly Detection via Neural Sparsification
Zheng Gong, Guifeng Wang, Ying Sun, et al.
(2023), pp. 2104-2113
Open Access | Times Cited: 6
UD-GNN
Yang Liu, Xiang Ao, Fuli Feng, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022), pp. 1131-1140
Closed Access | Times Cited: 10
F2GNN: An Adaptive Filter with Feature Segmentation for Graph-Based Fraud Detection
Guanghui Hu, Yang Liu, Qing He, et al.
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2024), pp. 6335-6339
Closed Access | Times Cited: 1
DiG-In-GNN: Discriminative Feature Guided GNN-Based Fraud Detector against Inconsistencies in Multi-Relation Fraud Graph
Jinghui Zhang, Zhengjia Xu, Dingyang Lv, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2024) Vol. 38, Iss. 8, pp. 9323-9331
Open Access | Times Cited: 1
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
An imbalanced learning method based on graph tran-smote for fraud detection
Jintao Wen, Xianghong Tang, Jianguang Lu
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 1
Doubly Robust AUC Optimization against Noisy and Adversarial Samples
Chenkang Zhang, Wanli Shi, Lei Luo, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2023), pp. 3195-3205
Closed Access | Times Cited: 3
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