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Detecting which nodes in graphs are outliers is a relatively new machine learning task with numerous applications.
Lof: identifying density-based local outliers
M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander · 2000
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Linguistic inquiry and word count: Liwc 2001
J. W. Pennebaker, M. E. Francis, and R. J. Booth · 2001
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Autopart: Parameter-free graph partitioning and outlier detection
D. Chakrabarti · 2004
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A (sub) graph isomorphism algorithm for matching large graphs
L. P. Cordella, P. Foggia, C. Sansone, and M. Vento · 2004
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The enron corpus: A new dataset for email classification research
B. Klimt and Y. Yang · 2004
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Statistical comparisons of classifiers over multiple data sets
J. Demšar · 2006
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Clustering in complex directed networks
G. Fagiolo · 2007
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The dynamics of viral marketing
J. Leskovec, L. A. Adamic, and B. A. Huberman · 2007
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Scan: a structural clustering algorithm for networks
X. Xu, N. Yuruk, Z. Feng, and T. A. Schweiger · 2007
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Collective classification in network data
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad · 2008
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Nus-wide: a real-world web image database from national university of singapore
T.-S. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y. Zheng · 2009
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Unifying guilt-by-association approaches: Theorems and fast algorithms
D. Koutra, T.-Y. Ke, U. Kang, D. H. P. Chau, H.-K. K. Pao, and C. Faloutsos · 2011
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Non-negative residual matrix factorization with application to graph anomaly detection
H. Tong and C.-Y. Lin · 2011
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Isolation-based anomaly detection
F. T. Liu, K. M. Ting, and Z.-H. Zhou · 2012
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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Ranking outlier nodes in subspaces of attributed graphs
E. Müller, P. I. Sánchez, Y. Mülle, and K. Böhm · 2013
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Statistical selection of congruent subspaces for mining attributed graphs
P. I. Sánchez, E. Müller, F. Laforet, F. Keller, and K. Böhm · 2013
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Anomaly detection using autoencoders with nonlinear dimensionality reduction
M. Sakurada and T. Yairi · 2014
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Graph based anomaly detection and description: a survey
L. Akoglu, H. Tong, and D. Koutra · 2015
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A meta-analysis of the anomaly detection problem
A. Emmott, S. Das, T. Dietterich, A. Fern, and W.-K. Wong · 2015
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Image-based recommendations on styles and substitutes
J. McAuley, C. Targett, Q. Shi, and A. Van Den Hengel · 2015
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Collective opinion spam detection: Bridging review networks and metadata
S. Rayana and L. Akoglu · 2015
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Discovering opinion spammer groups by network footprints
J. Ye and L. Akoglu · 2015
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On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
G. O. Campos, A. Zimek, J. Sander, R. J. Campello, B. Micenková, E. Schubert, I. Assent, and M. E. Houle · 2016
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Fraudar: Bounding graph fraud in the face of camouflage
B. Hooi, H. A. Song, A. Beutel, N. Shah, K. Shin, and C. Faloutsos · 2016
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Variational graph auto-encoders
T. N. Kipf and M. Welling · 2016
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An introduction to outlier analysis
C. C. Aggarwal · 2017
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Outlier detection with autoencoder ensembles
J. Chen, S. Sathe, C. Aggarwal, and D. Turaga · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Radar: Residual analysis for anomaly detection in attributed networks
J. Li, H. Dani, X. Hu, and H. Liu · 2017
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Anomaly detection with robust deep autoencoders
C. Zhou and R. C. Paffenroth · 2017
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Rev2: Fraudulent user prediction in rating platforms
S. Kumar, B. Hooi, D. Makhija, M. Kumar, C. Faloutsos, and V. Subrahmanian · 2018
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Anomalous: A joint modeling approach for anomaly detection on attributed networks
Z. Peng, M. Luo, J. Li, H. Liu, and Q. Zheng · 2018
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Pitfalls of graph neural network evaluation
O. Shchur, M. Mumme, A. Bojchevski, and S. Günnemann · 2018
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Deep structure learning for fraud detection
H. Wang, C. Zhou, J. Wu, W. Dang, X. Zhu, and J. Wang · 2018
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Outlier aware network embedding for attributed networks
S. Bandyopadhyay, N. Lokesh, and M. N. Murty · 2019
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Deep anomaly detection on attributed networks
K. Ding, J. Li, R. Bhanushali, and H. Liu · 2019
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Predicting dynamic embedding trajectory in temporal interaction networks
S. Kumar, X. Zhang, and J. Leskovec · 2019
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Training graph neural networks with 1000 layers
G. Li, M. Müller, B. Ghanem, and V. Koltun · 2021
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Anomaly detection on attributed networks via contrastive self-supervised learning
Y. Liu, Z. Li, S. Pan, C. Gong, C. Zhou, and G. Karypis · 2021
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A comprehensive survey on graph anomaly detection with deep learning
X. Ma, J. Wu, S. Xue, J. Yang, C. Zhou, Q. Z. Sheng, H. Xiong, and L. Akoglu · 2021
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Uncovering coordinated networks on social media: Methods and case studies
D. Pacheco, P.-M. Hui, C. Torres-Lugo, B. T. Truong, A. Flammini, and F. Menczer · 2021
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Benchmarking unsupervised outlier detection with realistic synthetic data
G. Steinbuss and K. Böhm · 2021
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One-class graph neural networks for anomaly detection in attributed networks
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M. Weber, G. Domeniconi, J. Chen, D. K. I. Weidele, C. Bellei, T. Robinson, and C. E. Leiserson · 2019
Cited alongside, same era.
Fast and accurate anomaly detection in dynamic graphs with a two-pronged approach
M. Yoon, B. Hooi, K. Shin, and C. Faloutsos · 2019
Cited alongside, same era.
Graphsaint: Graph sampling based inductive learning method
H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. Prasanna · 2019
Cited alongside, same era.
Lscp: Locally selective combination in parallel outlier ensembles
Y. Zhao, Z. Nasrullah, M. K. Hryniewicki, and Z. Li · 2019
Cited alongside, same era.
PyOD: A python toolbox for scalable outlier detection
Y. Zhao, Z. Nasrullah, and Z. Li · 2019
Cited alongside, same era.
Addgraph: Anomaly detection in dynamic graph using attention-based temporal gcn
L. Zheng, Z. Li, J. Li, Z. Li, and J. Gao · 2019
Cited alongside, same era.
Outlier resistant unsupervised deep architectures for attributed network embedding
S. Bandyopadhyay, S. V. Vivek, and M. Murty · 2020
Cited alongside, same era.
X. Wang, B. Jin, Y. Du, P. Cui, Y. Tan, and Y. Yang · 2021
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Decoupling representation learning and classification for gnn-based anomaly detection
Y. Wang, J. Zhang, S. Guo, H. Yin, C. Li, and H. Chen · 2021
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Count-gnn: Graph neural networks for subgraph isomorphism counting
X. Yu, Z. Liu, Y. Fang, and X. Zhang · 2021
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Higher-order structure based anomaly detection on attributed networks
X. Yuan, N. Zhou, S. Yu, H. Huang, Z. Chen, and F. Xia · 2021
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Fraudre: Fraud detection dual-resistant to graph inconsistency and imbalance
G. Zhang, J. Wu, J. Yang, A. Beheshti, S. Xue, C. Zhou, and Q. Z. Sheng · 2021
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On using classification datasets to evaluate graph outlier detection: Peculiar observations and new insights
L. Zhao and L. Akoglu · 2021
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A synergistic approach for graph anomaly detection with pattern mining and feature learning
T. Zhao, T. Jiang, N. Shah, and M. Jiang · 2021
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TOD: Tensor-based outlier detection
Y. Zhao, G. H. Chen, and Z. Jia · 2021
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SUOD: accelerating large-scale unsupervised heterogeneous outlier detection
Y. Zhao, X. Hu, C. Cheng, C. Wang, C. Wan, W. Wang, J. Yang, H. Bai, Z. Li, C. Xiao, Y. Wang, Z. Qiao, J. Sun, and L. Akoglu · 2021
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Automatic unsupervised outlier model selection
Y. Zhao, R. Rossi, and L. Akoglu · 2021
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Graph robustness benchmark: Benchmarking the adversarial robustness of graph machine learning
Q. Zheng, X. Zou, Y. Dong, Y. Cen, D. Yin, J. Xu, Y. Yang, and J. Tang · 2021
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An empirical study of graph contrastive learning
Y. Zhu, Y. Xu, Q. Liu, and S. Wu · 2021
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Meta propagation networks for graph few-shot semi-supervised learning
K. Ding, J. Wang, J. Caverlee, and H. Liu · 2022
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Data augmentation for deep graph learning: A survey
K. Ding, Z. Xu, H. Tong, and H. Liu · 2022
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X. Ding, L. Zhao, and L. Akoglu · 2022
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Adbench: Anomaly detection benchmark
S. Han, X. Hu, H. Huang, M. Jiang, and Y. Zhao · 2022
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Dgraph: A large-scale financial dataset for graph anomaly detection
X. Huang, Y. Yang, Y. Wang, C. Wang, Z. Zhang, J. Xu, and L. Chen · 2022
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On equivalence of anomaly detection algorithms
C. I. Jerez, J. Zhang, and M. R. Silva · 2022
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How to find your friendly neighborhood: Graph attention design with self-supervision
D. Kim and A. Oh · 2022
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Ecod: Unsupervised outlier detection using empirical cumulative distribution functions
Z. Li, Y. Zhao, X. Hu, N. Botta, C. Ionescu, and G. Chen · 2022
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PyGOD: A python library for graph outlier detection
K. Liu, Y. Dou, Y. Zhao, X. Ding, X. Hu, R. Zhang, K. Ding, C. Chen, H. Peng, K. Shu, et al · 2022
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Autogml: Fast automatic model selection for graph machine learning
N. Park, R. Rossi, N. Ahmed, and C. Faloutsos · 2022
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Raising the bar in graph-level anomaly detection, 2022
C. Qiu, M. Kloft, S. Mandt, and M. Rudolph · 2022
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Contrastive attributed network anomaly detection with data augmentation
Z. Xu, X. Huang, Y. Zhao, Y. Dong, and J. Li · 2022
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Towards similarity-aware time-series classification
D. Zha, K.-H. Lai, K. Zhou, and X. Hu · 2022
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efraudcom: An e-commerce fraud detection system via competitive graph neural networks
G. Zhang, Z. Li, J. Huang, J. Wu, C. Zhou, J. Yang, and J. Gao · 2022
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Towards unsupervised hpo for outlier detection
Y. Zhao and L. Akoglu · 2022
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