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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:

Graph based anomaly detection and description: a survey
Leman Akoglu, Hanghang Tong, Danai Koutra
Data Mining and Knowledge Discovery (2014) Vol. 29, Iss. 3, pp. 626-688
Closed Access | Times Cited: 1233

Showing 1-25 of 1233 citing articles:

Deep Learning for Anomaly Detection
Guansong Pang, Chunhua Shen, Longbing Cao, et al.
ACM Computing Surveys (2021) Vol. 54, Iss. 2, pp. 1-38
Open Access | Times Cited: 1518
A Survey on Network Embedding
Peng Cui, Xiao Wang, Jian Pei, et al.
IEEE Transactions on Knowledge and Data Engineering (2018) Vol. 31, Iss. 5, pp. 833-852
Open Access | Times Cited: 1197
Graph convolutional networks: a comprehensive review
Si Zhang, Hanghang Tong, Jiejun Xu, et al.
Computational Social Networks (2019) Vol. 6, Iss. 1
Open Access | Times Cited: 1139
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, et al.
Medical Image Analysis (2019) Vol. 54, pp. 30-44
Closed Access | Times Cited: 1110
Knowledge graph refinement: A survey of approaches and evaluation methods
Heiko Paulheim
Semantic Web (2016) Vol. 8, Iss. 3, pp. 489-508
Closed Access | Times Cited: 1079
A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data
Markus Goldstein, Seiichi Uchida
PLoS ONE (2016) Vol. 11, Iss. 4, pp. e0152173-e0152173
Open Access | Times Cited: 862
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff, Jacob Kauffmann, Robert A. Vandermeulen, et al.
Proceedings of the IEEE (2021) Vol. 109, Iss. 5, pp. 756-795
Open Access | Times Cited: 690
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
Guilherme O. Campos, Arthur Zimek, Jörg Sander, et al.
Data Mining and Knowledge Discovery (2016) Vol. 30, Iss. 4, pp. 891-927
Closed Access | Times Cited: 648
Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives
Yassine Himeur, Khalida Ghanem, Abdullah Alsalemi, et al.
Applied Energy (2021) Vol. 287, pp. 116601-116601
Open Access | Times Cited: 431
Progress in Outlier Detection Techniques: A Survey
Hongzhi Wang, Mohamed Jaward Bah, Mohamed Hammad
IEEE Access (2019) Vol. 7, pp. 107964-108000
Open Access | Times Cited: 425
Community detection in networks: A multidisciplinary review
Muhammad Aqib Javed, Muhammad Shahzad Younis, Siddique Latif, et al.
Journal of Network and Computer Applications (2018) Vol. 108, pp. 87-111
Closed Access | Times Cited: 411
Real-time big data processing for anomaly detection: A Survey
Riyaz Ahamed Ariyaluran Habeeb, Fariza Hanum Nasaruddin, Abdullah Gani, et al.
International Journal of Information Management (2018) Vol. 45, pp. 289-307
Open Access | Times Cited: 405
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
Anomaly detection in dynamic networks: a survey
Stephen Ranshous, Shitian Shen, Danai Koutra, et al.
Wiley Interdisciplinary Reviews Computational Statistics (2015) Vol. 7, Iss. 3, pp. 223-247
Open Access | Times Cited: 339
REV2
Srijan Kumar, Bryan Hooi, Disha Makhija, et al.
(2018)
Open Access | Times Cited: 308
Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques
Seyed Mohammad Ghaffarian, Hamid Reza Shahriari
ACM Computing Surveys (2017) Vol. 50, Iss. 4, pp. 1-36
Closed Access | Times Cited: 297
Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey
Fannia Pacheco, Ernesto Expósito, Mathieu Gineste, et al.
IEEE Communications Surveys & Tutorials (2018) Vol. 21, Iss. 2, pp. 1988-2014
Open Access | Times Cited: 296
Fraud detection: A systematic literature review of graph-based anomaly detection approaches
Tahereh Pourhabibi, Kok‐Leong Ong, Booi Kam, et al.
Decision Support Systems (2020) Vol. 133, pp. 113303-113303
Open Access | Times Cited: 295
Misinformation in Social Media
Liang Wu, Fred Morstatter, Kathleen M. Carley, et al.
ACM SIGKDD Explorations Newsletter (2019) Vol. 21, Iss. 2, pp. 80-90
Closed Access | Times Cited: 290
NetWalk
Wenchao Yu, Wei Cheng, Charų C. Aggarwal, et al.
(2018), pp. 2672-2681
Open Access | Times Cited: 289
A comprehensive survey of anomaly detection techniques for high dimensional big data
Srikanth Thudumu, Philip Branch, Jiong Jin, et al.
Journal Of Big Data (2020) Vol. 7, Iss. 1
Open Access | Times Cited: 289
APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions
Véronique Van Vlasselaer, Cristián Bravo, Olivier Caelen, et al.
Decision Support Systems (2015) Vol. 75, pp. 38-48
Open Access | Times Cited: 285
A survey on anomaly detection for technical systems using LSTM networks
B. Lindemann, Benjamin Maschler, Nada Sahlab, et al.
Computers in Industry (2021) Vol. 131, pp. 103498-103498
Open Access | Times Cited: 260
Social network security: Issues, challenges, threats, and solutions
Shailendra Rathore, Pradip Kumar Sharma, Vincenzo Loia, et al.
Information Sciences (2017) Vol. 421, pp. 43-69
Closed Access | Times Cited: 235
Attention Models in Graphs
John Boaz Lee, Ryan A. Rossi, Sungchul Kim, et al.
ACM Transactions on Knowledge Discovery from Data (2019) Vol. 13, Iss. 6, pp. 1-25
Closed Access | Times Cited: 222
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