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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:

Interactive Anomaly Detection on Attributed Networks
Kaize Ding, Jundong Li, Huan Liu
(2019)
Open Access | Times Cited: 126

Showing 1-25 of 126 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
Deep Anomaly Detection on Attributed Networks
Kaize Ding, Jundong Li, Rohit Bhanushali, et al.
Society for Industrial and Applied Mathematics eBooks (2019), pp. 594-602
Open Access | Times Cited: 331
Next-item Recommendation with Sequential Hypergraphs
Jianling Wang, Kaize Ding, Liangjie Hong, et al.
(2020)
Closed Access | Times Cited: 225
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
Yixin Liu, Zhao Li, Shirui Pan, et al.
IEEE Transactions on Neural Networks and Learning Systems (2021) Vol. 33, Iss. 6, pp. 2378-2392
Open Access | Times Cited: 215
Survey on Applications of Multi-Armed and Contextual Bandits
Djallel Bouneffouf, Irina Rish, Charų C. Aggarwal
2022 IEEE Congress on Evolutionary Computation (CEC) (2020), pp. 1-8
Closed Access | Times Cited: 179
A Survey on Practical Applications of Multi-Armed and Contextual Bandits
Djallel Bouneffouf, Irina Rish
arXiv (Cornell University) (2019)
Open Access | Times Cited: 103
Graph Prototypical Networks for Few-shot Learning on Attributed Networks
Kaize Ding, Jianling Wang, Jundong Li, et al.
(2020)
Open Access | Times Cited: 103
Transferring Robustness for Graph Neural Network Against Poisoning Attacks
Xianfeng Tang, Yandong Li, Yiwei Sun, et al.
(2020)
Open Access | Times Cited: 81
Few-shot Network Anomaly Detection via Cross-network Meta-learning
Kaize Ding, Qinghai Zhou, Hanghang Tong, et al.
(2021)
Open Access | Times Cited: 81
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: 41
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
Alleviating Structural Distribution Shift in Graph Anomaly Detection
Yuan Gao, Xiang Wang, Xiangnan He, et al.
(2023)
Open Access | Times Cited: 28
GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction
Amit Roy, Juan Shu, Jia Li, et al.
(2024)
Open Access | Times Cited: 10
Trustworthy AI-based Performance Diagnosis Systems for Cloud Applications: A Review
Ruyue Xin, Jingye Wang, Peng Chen, et al.
ACM Computing Surveys (2025) Vol. 57, Iss. 5, pp. 1-37
Open Access | Times Cited: 1
Inductive Anomaly Detection on Attributed Networks
Kaize Ding, Jundong Li, Nitin Agarwal, et al.
(2020), pp. 1288-1294
Open Access | Times Cited: 61
Graph Few-shot Class-incremental Learning
Zhen Tan, Kaize Ding, Ruocheng Guo, et al.
Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (2022), pp. 987-996
Open Access | Times Cited: 29
A Survey of Graph-Based Deep Learning for Anomaly Detection in Distributed Systems
Armin Danesh Pazho, Ghazal Alinezhad Noghre, Arnab A Purkayastha, et al.
IEEE Transactions on Knowledge and Data Engineering (2023) Vol. 36, Iss. 1, pp. 1-20
Open Access | Times Cited: 21
Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning
Daochen Zha, Kwei-Herng Lai, Mingyang Wan, et al.
2021 IEEE International Conference on Data Mining (ICDM) (2020), pp. 771-780
Open Access | Times Cited: 42
ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks
Yulong Pei, Tianjin Huang, Werner van Ipenburg, et al.
Machine Learning (2021) Vol. 111, Iss. 2, pp. 519-541
Open Access | Times Cited: 36
DAGAD: Data Augmentation for Graph Anomaly Detection
Fanzhen Liu, Xiaoxiao Ma, Jia Wu, et al.
2021 IEEE International Conference on Data Mining (ICDM) (2022), pp. 259-268
Open Access | Times Cited: 24
DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks
Wasim Khan, Mohammad Haroon, Ahmad Neyaz Khan, et al.
IEEE Access (2022) Vol. 10, pp. 91160-91176
Open Access | Times Cited: 22
Hop-Count Based Self-supervised Anomaly Detection on Attributed Networks
Tianjin Huang, Yulong Pei, Vlado Menkovski, et al.
Lecture notes in computer science (2023), pp. 225-241
Closed Access | Times Cited: 13
Few-Shot Graph Anomaly Detection via Dual-Level Knowledge Distillation
Xuan Li, Defu Cheng, L H Zhang, et al.
Entropy (2025) Vol. 27, Iss. 1, pp. 28-28
Open Access
Temporal multivariate-factors independence convolution network for anomaly detection in dynamic networks
Yang Yu, Minglai Shao, Xin Li, et al.
Neurocomputing (2025), pp. 129439-129439
Closed Access
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
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