TY - GEN
T1 - Contrastive Attributed Network Anomaly Detection with Data Augmentation
AU - Xu, Zhiming
AU - Huang, Xiao
AU - Zhao, Yue
AU - Dong, Yushun
AU - Li, Jundong
N1 - Funding Information:
Acknowledgements. Yushun Dong and Jundong Li are partially supported by the National Science Foundation (NSF) under grants #2006844.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022/5
Y1 - 2022/5
N2 - Attributed networks are a type of graph structured data used in many real-world scenarios. Detecting anomalies on attributed networks has a wide spectrum of applications such as spammer detection and fraud detection. Although this research area draws increasing attention in the last few years, previous works are mostly unsupervised because of expensive costs of labeling ground truth anomalies. Many recent studies have shown different types of anomalies are often mixed together on attributed networks and such invaluable human knowledge could provide complementary insights in advancing anomaly detection on attributed networks. To this end, we study the novel problem of modeling and integrating human knowledge of different anomaly types for attributed network anomaly detection. Specifically, we first model prior human knowledge through a novel data augmentation strategy. We then integrate the modeled knowledge in a Siamese graph neural network encoder through a well-designed contrastive loss. In the end, we train a decoder to reconstruct the original networks from the node representations learned by the encoder, and rank nodes according to its reconstruction error as the anomaly metric. Experiments on five real-world datasets demonstrate that the proposed framework outperforms the state-of-the-art anomaly detection algorithms.
AB - Attributed networks are a type of graph structured data used in many real-world scenarios. Detecting anomalies on attributed networks has a wide spectrum of applications such as spammer detection and fraud detection. Although this research area draws increasing attention in the last few years, previous works are mostly unsupervised because of expensive costs of labeling ground truth anomalies. Many recent studies have shown different types of anomalies are often mixed together on attributed networks and such invaluable human knowledge could provide complementary insights in advancing anomaly detection on attributed networks. To this end, we study the novel problem of modeling and integrating human knowledge of different anomaly types for attributed network anomaly detection. Specifically, we first model prior human knowledge through a novel data augmentation strategy. We then integrate the modeled knowledge in a Siamese graph neural network encoder through a well-designed contrastive loss. In the end, we train a decoder to reconstruct the original networks from the node representations learned by the encoder, and rank nodes according to its reconstruction error as the anomaly metric. Experiments on five real-world datasets demonstrate that the proposed framework outperforms the state-of-the-art anomaly detection algorithms.
KW - Anomaly detection
KW - Graph neural networks
KW - Self-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85130277969&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-05936-0_35
DO - 10.1007/978-3-031-05936-0_35
M3 - Conference article published in proceeding or book
SN - 9783031059353
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 444
EP - 457
BT - Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Proceedings
A2 - Gama, João
A2 - Li, Tianrui
A2 - Yu, Yang
A2 - Chen, Enhong
A2 - Zheng, Yu
A2 - Teng, Fei
T2 - 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2022)
Y2 - 16 May 2022 through 19 May 2022
ER -