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Graph-Level Anomaly Detection via Hierarchical Memory Networks

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Machine Learning and Knowledge Discovery in Databases: Research Track (ECML PKDD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14169))

Abstract

Graph-level anomaly detection aims to identify abnormal graphs that exhibit deviant structures and node attributes compared to the majority in a graph set. One primary challenge is to learn normal patterns manifested in both fine-grained and holistic views of graphs for identifying graphs that are abnormal in part or in whole. To tackle this challenge, we propose a novel approach called Hierarchical Memory Networks (HimNet), which learns hierarchical memory modules—node and graph memory modules—via a graph autoencoder network architecture. The node-level memory module is trained to model fine-grained, internal graph interactions among nodes for detecting locally abnormal graphs, while the graph-level memory module is dedicated to the learning of holistic normal patterns for detecting globally abnormal graphs. The two modules are jointly optimized to detect both locally- and globally-anomalous graphs. Extensive empirical results on 16 real-world graph datasets from various domains show that i) HimNet significantly outperforms the state-of-art methods and ii) it is robust to anomaly contamination. Codes are available at: https://github.com/Niuchx/HimNet.

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Notes

  1. 1.

    All the graph datasets are available on https://chrsmrrs.github.io/datasets/docs/datasets/ except hERG which is obtained from https://tdcommons.ai/single_pred_tasks/tox/.

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Acknowledgment

This work is partially supported by Australian Research Council under Grant DP210101347.

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Authors and Affiliations

  1. AAII, University of Technology Sydney, Sydney, Australia

    Chaoxi Niu & Ling Chen

  2. Singapore Management University, Singapore, Singapore

    Guansong Pang

Authors
  1. Chaoxi Niu
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  2. Guansong Pang
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  3. Ling Chen
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Corresponding author

Correspondence to Guansong Pang .

Editor information

Editors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Danai Koutra

  2. University of Vienna, Vienna, Austria

    Claudia Plant

  3. Max Planck Institute for Software Systems, Kaiserslautern, Germany

    Manuel Gomez Rodriguez

  4. Politecnico di Torino, Turin, Italy

    Elena Baralis

  5. CENTAI, Turin, Italy

    Francesco Bonchi

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Ethical Statement

In this work, we study the problem of graph-level anomaly detection which aims to identify abnormal graphs that exhibit unusual patterns in comparison to the majority in a graph set. Since graphs are widely used in various domains, anomaly detection on graphs has broad applications, such as identifying toxic molecules from chemical compound graphs and recognizing abnormal internet activity graphs. To capture the hierarchical normal patterns of graph data, we propose hierarchical memory networks to learn node and graph memory modules. The proposed method enables the detection of both locally and globally anomalous graphs. For all the used data sets in this paper, there is no private personally identifiable information or offensive content. However, when using the proposed method for solving realistic problems, it is essential to ensure that appropriate measures are taken to protect the privacy of individuals. This may include anonymizing data, limiting access to sensitive information, or obtaining informed consent from individuals before collecting their data.

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Niu, C., Pang, G., Chen, L. (2023). Graph-Level Anomaly Detection via Hierarchical Memory Networks. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14169. Springer, Cham. https://doi.org/10.1007/978-3-031-43412-9_12

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  • DOI: https://doi.org/10.1007/978-3-031-43412-9_12

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