Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
2-2022
Abstract
Graph-level anomaly detection (GAD) describes the problem of detecting graphs that are abnormal in their structure and/or the features of their nodes, as compared to other graphs. One of the challenges in GAD is to devise graph representations that enable the detection of both locally- and globally-anomalous graphs, i.e., graphs that are abnormal in their fine-grained (node-level) or holistic (graph-level) properties, respectively. To tackle this challenge we introduce a novel deep anomaly detection approach for GAD that learns rich global and local normal pattern information by joint random distillation of graph and node representations. The random distillation is achieved by training one GNN to predict another GNN with randomly initialized network weights. Extensive experiments on 16 real-world graph datasets from diverse domains show that our model significantly outperforms seven state-of-the-art models. Code and datasets are available at https://git.io/GLocalKD.
Keywords
Graph-level anomaly detection, Graph neural networks, Knowledge distillation, Deep learning
Discipline
Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 15th ACM International Conference on Web Search and Data Mining, Virtual Conference, 2022 February 21-25
First Page
704
Last Page
714
ISBN
9781450391320
Identifier
10.1145/3488560.3498473
Publisher
ACM
City or Country
Virtual Conference
Citation
MA, Rongrong; PANG, Guansong; CHEN, Ling; and HENGEL, Anton Van Den.
Deep graph-level anomaly detection by glocal knowledge distillation. (2022). Proceedings of the 15th ACM International Conference on Web Search and Data Mining, Virtual Conference, 2022 February 21-25. 704-714.
Available at: https://ink.library.smu.edu.sg/sis_research/7054
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Artificial Intelligence and Robotics Commons, Graphics and Human Computer Interfaces Commons