Learning Node Abnormality with Weak Supervision

Qinghai Zhou, Kaize Ding, Huan Liu, Hanghang Tong

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Graph anomaly detection aims to identify the atypical substructures and has attracted an increasing amount of research attention due to its profound impacts in a variety of application domains, including social network analysis, security, finance, and many more. The lack of prior knowledge of the ground-truth anomaly has been a major obstacle in acquiring fine-grained annotations (e.g., anomalous nodes), therefore, a plethora of existing methods have been developed either with a limited number of node-level supervision or in an unsupervised manner. Nonetheless, annotations for coarse-grained graph elements (e.g., a suspicious group of nodes), which often require marginal human effort in terms of time and expertise, are comparatively easier to obtain. Therefore, it is appealing to investigate anomaly detection in a weakly-supervised setting and to establish the intrinsic relationship between annotations at different levels of granularity. In this paper, we tackle the challenging problem of weakly-supervised graph anomaly detection with coarse-grained supervision by (1) proposing a novel architecture of graph neural network with attention mechanism named Wedge that can identify the critical node-level anomaly given a few labels of anomalous subgraphs, and (2) designing a novel objective with contrastive loss that facilitates node representation learning by enforcing distinctive representations between normal and abnormal graph elements. Through extensive evaluations on real-world datasets, we corroborate the efficacy of our proposed method, improving AUC-ROC by up to 16.48% compared to the best competitor.

Original languageEnglish (US)
Title of host publicationCIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages11
ISBN (Electronic)9798400701245
StatePublished - Oct 21 2023
Event32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 - Birmingham, United Kingdom
Duration: Oct 21 2023Oct 25 2023

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Conference32nd ACM International Conference on Information and Knowledge Management, CIKM 2023
Country/TerritoryUnited Kingdom


  • Graph anomaly detection
  • Weak supervision

ASJC Scopus subject areas

  • General Business, Management and Accounting
  • General Decision Sciences


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