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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:

What Happens Behind the Scene? Towards Fraud Community Detection in E-Commerce from Online to Offline
Li Zhao, Pengrui Hui, Peng Zhang, et al.
Companion Proceedings of the The Web Conference 2018 (2021), pp. 105-113
Closed Access | Times Cited: 16

Showing 16 citing articles:

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: 9
RHGNN: Fake reviewer detection based on reinforced heterogeneous graph neural networks
Jun Zhao, Minglai Shao, Hailiang Tang, et al.
Knowledge-Based Systems (2023) Vol. 280, pp. 111029-111029
Closed Access | Times Cited: 14
CoDÆN: Benchmarks and Comparison of Evolutionary Community Detection Algorithms for Dynamic Networks
Giordano Paoletti, Luca Gioacchini, Marco Mellia, et al.
ACM Transactions on the Web (2025)
Closed Access
Trust in Social Commerce: Challenges and Opportunities for Building Consumer Confidence and Shaping Purchase Intention
G. Lee
International Journal of Applied Research in Business and Management (2025) Vol. 6, Iss. 1
Open Access
A graph-powered large-scale fraud detection system
Zhao Li, Biao Wang, Jiaming Huang, et al.
International Journal of Machine Learning and Cybernetics (2023) Vol. 15, Iss. 1, pp. 115-128
Closed Access | Times Cited: 9
A Review of Financial Fraud Detection in E-Commerce Using Machine Learning
Abhay Narayan, Sheo Kumar, Anu Mary Chacko
Smart innovation, systems and technologies (2023), pp. 237-248
Closed Access | Times Cited: 6
Accelerating Maximal Bicliques Enumeration with GPU on large scale network
Chunqi Wu, Jingdong Li, Zhao Li, et al.
Future Generation Computer Systems (2024) Vol. 161, pp. 601-613
Closed Access | Times Cited: 1
Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck
Sangwoo Seo, Sungwon Kim, Jihyeong Jung, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2024), pp. 2572-2583
Open Access | Times Cited: 1
FinBrain 2.0: when finance meets trustworthy AI
Jun Zhou, Chaochao Chen, Longfei Li, et al.
Frontiers of Information Technology & Electronic Engineering (2022) Vol. 23, Iss. 12, pp. 1747-1764
Closed Access | Times Cited: 6
Towards Cross-Lingual Multi-Modal Misinformation Detection for E-Commerce Management
Yifan He, Zhao Li, Zhenpeng Li, et al.
IEEE Transactions on Network and Service Management (2023) Vol. 20, Iss. 2, pp. 1040-1050
Closed Access | Times Cited: 2
Community reinforcement: An effective and efficient preprocessing method for accurate community detection
Yoonsuk Kang, Jun‐Seok Lee, Won-Yong Shin, et al.
Knowledge-Based Systems (2021) Vol. 237, pp. 107741-107741
Closed Access | Times Cited: 5
Edge-Based Minimal k-Core Subgraph Search
Wang Ting, Yu Jiang, Jianye Yang, et al.
Mathematics (2023) Vol. 11, Iss. 15, pp. 3407-3407
Open Access | Times Cited: 1
AMBEA: Aggressive Maximal Biclique Enumeration in Large Bipartite Graph Computing
Zhe Pan, Xu Li, Shuibing He, et al.
IEEE Transactions on Computers (2024) Vol. 73, Iss. 12, pp. 2664-2677
Closed Access
A Heterogeneous Graph-based Fraudulent Community Detection System
Anting Zhang, Bin Wu, Yinsheng Li
(2021), pp. 43-48
Closed Access | Times Cited: 2
Semi-supervised Community Detection using Graph Embedding
Bin Wu, Xinyu Yao, Boyan Zhang
(2022) Vol. 2008, pp. 141-147
Closed Access | Times Cited: 1
Detecting Group Behavior for Anti-Money Laundering With Incomplete Network Information
Pavlo Tertychnyi, Tommy Lindstrom, Changling Liu, et al.
2021 IEEE International Conference on Big Data (Big Data) (2022), pp. 2383-2388
Closed Access
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