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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:

Representation Learning in Graphs for Credit Card Fraud Detection
Rafaël Van Belle, Sandra Mitrović, Jochen De Weerdt
Lecture notes in computer science (2020), pp. 32-46
Closed Access | Times Cited: 10

Showing 10 citing articles:

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: 26
E2CNN: entity-type-enriched cascaded neural network for Chinese financial relation extraction
Mengfan Li, Xuanhua Shi, C. F. Qiao, et al.
Frontiers of Computer Science (2025) Vol. 19, Iss. 10
Open Access
Credit Card Fraud Detection Technique by Applying Graph Database Model
Debachudamani Prusti, Daisy Das, Santanu Kumar Rath
Arabian Journal for Science and Engineering (2021) Vol. 46, Iss. 9, pp. 1-20
Closed Access | Times Cited: 25
Inductive Link Prediction Banking Fraud Detection System Using Homogeneous Graph-Based Machine Learning Model
Hilmi Aziz Bukhori, Rinaldi Munir
2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) (2023), pp. 0246-0251
Closed Access | Times Cited: 7
Temporally-aware node embeddings for evolving networks topologies
Karen B. Enes, Matheus Nunes, Fabrício Murai, et al.
AI Communications (2024) Vol. 37, Iss. 3, pp. 411-428
Closed Access
A Graph-Based Semi-Supervised Fraud Detection Framework
Rongrong Jing, Xiaolong Zheng, Hu Tian, et al.
(2019) Vol. 28, pp. 1-5
Closed Access | Times Cited: 3
Hybridizing graph‐based Gaussian mixture model with machine learning for classification of fraudulent transactions
Debachudamani Prusti, Ranjan Kumar Behera, Santanu Kumar Rath
Computational Intelligence (2022) Vol. 38, Iss. 6, pp. 2134-2160
Closed Access | Times Cited: 2
Identifying Fraud Rings Using Domain Aware Weighted Community Detection
Shaik Masihullah, Meghana Negi, Jose Matthew, et al.
Lecture notes in computer science (2022), pp. 150-167
Closed Access | Times Cited: 1
An Graph Neural Network and Feature Interaction Based Fraud Detection
Zongli Jiang, Jieru Liang, Nan Ma
2021 17th International Conference on Computational Intelligence and Security (CIS) (2021), pp. 464-468
Closed Access | Times Cited: 1
A Comparative Study of Representation Learning Techniques for Dynamic Networks
Carlos Ortega Vázquez, Sandra Mitrović, Jochen De Weerdt, et al.
Advances in intelligent systems and computing (2020), pp. 523-530
Closed Access
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