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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:

Inductive Anomaly Detection on Attributed Networks
Kaize Ding, Jundong Li, Nitin Agarwal, et al.
(2020), pp. 1288-1294
Open Access | Times Cited: 61

Showing 1-25 of 61 citing articles:

A Comprehensive Survey on Graph Anomaly Detection With Deep Learning
Xiaoxiao Ma, Jia Wu, Shan Xue, et al.
IEEE Transactions on Knowledge and Data Engineering (2021) Vol. 35, Iss. 12, pp. 12012-12038
Open Access | Times Cited: 400
Graph Self-Supervised Learning: A Survey
Yixin Liu, Ming Jin, Shirui Pan, et al.
IEEE Transactions on Knowledge and Data Engineering (2022), pp. 1-1
Open Access | Times Cited: 352
eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks
Ge Zhang, Zhao Li, Jiaming Huang, et al.
ACM transactions on office information systems (2022) Vol. 40, Iss. 3, pp. 1-29
Closed Access | Times Cited: 97
Graph Prototypical Networks for Few-shot Learning on Attributed Networks
Kaize Ding, Jianling Wang, Jundong Li, et al.
(2020)
Open Access | Times Cited: 106
Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection
Yu Zheng, Ming Jin, Yixin Liu, et al.
IEEE Transactions on Knowledge and Data Engineering (2021) Vol. 35, Iss. 12, pp. 12220-12233
Open Access | Times Cited: 85
Few-shot Network Anomaly Detection via Cross-network Meta-learning
Kaize Ding, Qinghai Zhou, Hanghang Tong, et al.
(2021)
Open Access | Times Cited: 81
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
Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation
Rongrong Ma, Guansong Pang, Ling Chen, et al.
Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (2022), pp. 704-714
Open Access | Times Cited: 50
ComGA
Xuexiong Luo, Jia Wu, Amin Beheshti, et al.
Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (2022), pp. 657-665
Closed Access | Times Cited: 42
Contrastive Attributed Network Anomaly Detection with Data Augmentation
Zhiming Xu, Xiao Huang, Yue Zhao, et al.
Lecture notes in computer science (2022), pp. 444-457
Closed Access | Times Cited: 42
GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection
Yixin Liu, Kaize Ding, Huan Liu, et al.
(2023), pp. 339-347
Open Access | Times Cited: 26
Graph Few-shot Learning with Attribute Matching
Ning Wang, Minnan Luo, Kaize Ding, et al.
(2020)
Closed Access | Times Cited: 50
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks
Jiaqiang Zhang, Senzhang Wang, Songcan Chen
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (2022), pp. 2376-2382
Open Access | Times Cited: 27
Task-Adaptive Few-shot Node Classification
Song Wang, Kaize Ding, Chuxu Zhang, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022), pp. 1910-1919
Open Access | Times Cited: 25
Deep graph level anomaly detection with contrastive learning
Xuexiong Luo, Jia Wu, Jian Yang, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 24
Raising the Bar in Graph-level Anomaly Detection
Chen Qiu, Marius Kloft, Stephan Mandt, et al.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (2022), pp. 2196-2203
Open Access | Times Cited: 22
ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection
Junwei He, Qianqian Xu, Yangbangyan Jiang, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2024) Vol. 38, Iss. 8, pp. 8481-8489
Open Access | Times Cited: 5
Attribute graph anomaly detection utilizing memory networks enhanced by multi-embedding comparison
Lianming Zhang, Baolin Wu, Pingping Dong
Neurocomputing (2025), pp. 129762-129762
Closed Access
DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction
Hossein Rafiee Zade, Hadi Zare, Mohsen Ghassemi Parsa, et al.
Studies in computational intelligence (2025), pp. 3-15
Closed Access
A Synergistic Approach for Graph Anomaly Detection With Pattern Mining and Feature Learning
Tong Zhao, Tianwen Jiang, Neil Shah, et al.
IEEE Transactions on Neural Networks and Learning Systems (2021) Vol. 33, Iss. 6, pp. 2393-2405
Open Access | Times Cited: 32
Semi-supervised Anomaly Detection on Attributed Graphs
Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
2022 International Joint Conference on Neural Networks (IJCNN) (2021), pp. 1-8
Open Access | Times Cited: 28
Cross-Domain Graph Anomaly Detection
Kaize Ding, Kai Shu, Xuan Shan, et al.
IEEE Transactions on Neural Networks and Learning Systems (2021) Vol. 33, Iss. 6, pp. 2406-2415
Closed Access | Times Cited: 27
Few-shot Node Classification with Extremely Weak Supervision
Song Wang, Yushun Dong, Kaize Ding, et al.
(2023)
Open Access | Times Cited: 12
Dual-channel graph-level anomaly detection method based on multi-graph representation learning
Yongjun Jing, Hao Wang, Jiale Chen, et al.
Applied Intelligence (2025) Vol. 55, Iss. 6
Closed Access
Higher-order Structure Based Anomaly Detection on Attributed Networks
Xu Yuan, N. Zhou, Shuo Yu, et al.
2021 IEEE International Conference on Big Data (Big Data) (2021)
Open Access | Times Cited: 26
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