GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection

Yixin Liu, Kaize Ding, Huan Liu, Shirui Pan

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

24 Scopus citations

Abstract

Most existing deep learning models are trained based on the closed-world assumption, where the test data is assumed to be drawn i.i.d. from the same distribution as the training data, known as in-distribution (ID). However, when models are deployed in an open-world scenario, test samples can be out-of-distribution (OOD) and therefore should be handled with caution. To detect such OOD samples drawn from unknown distribution, OOD detection has received increasing attention lately. However, current endeavors mostly focus on grid-structured data and its application for graph-structured data remains under-explored. Considering the fact that data labeling on graphs is commonly time-expensive and labor-intensive, in this work we study the problem of unsupervised graph OOD detection, aiming at detecting OOD graphs solely based on unlabeled ID data. To achieve this goal, we develop a new graph contrastive learning framework GOOD-D for detecting OOD graphs without using any ground-truth labels. By performing hierarchical contrastive learning on the augmented graphs generated by our perturbation-free graph data augmentation method, GOOD-D is able to capture the latent ID patterns and accurately detect OOD graphs based on the semantic inconsistency in different granularities (i.e., node-level, graph-level, and group-level). As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods. The experiment results demonstrate the superiority of our approach over different methods on various datasets.

Original languageEnglish (US)
Title of host publicationWSDM 2023 - Proceedings of the 16th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages339-347
Number of pages9
ISBN (Electronic)9781450394079
DOIs
StatePublished - Feb 27 2023
Event16th ACM International Conference on Web Search and Data Mining, WSDM 2023 - Singapore, Singapore
Duration: Feb 27 2023Mar 3 2023

Publication series

NameWSDM 2023 - Proceedings of the 16th ACM International Conference on Web Search and Data Mining

Conference

Conference16th ACM International Conference on Web Search and Data Mining, WSDM 2023
Country/TerritorySingapore
CitySingapore
Period2/27/233/3/23

Keywords

  • contrastive learning
  • graph neural networks
  • out-of-distribution detection

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Software

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