Contrastive Attributed Network Anomaly Detection with Data Augmentation

Zhiming Xu, Xiao Huang, Yue Zhao, Yushun Dong, Jundong Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

9 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Proceedings
EditorsJoão Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, Fei Teng
Number of pages14
Publication statusPublished - May 2022
Event26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2022) - Chengdu, China
Duration: 16 May 202219 May 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13281 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2022)


  • Anomaly detection
  • Graph neural networks
  • Self-supervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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