research-article

Error-Bounded Graph Anomaly Loss for GNNs

Authors:

Meng JiangAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 1873 - 1882

https://doi.org/10.1145/3340531.3411979

Published: 19 October 2020 Publication History

Abstract

Graph neural networks (GNNs) have been widely used to learn node representations from graph data in an unsupervised way for downstream tasks. However, when applied to detect anomalies (e.g., outliers, unexpected density), they deliver unsatisfactory performance as existing loss functions fail. For example, any loss based on random walk (RW) algorithms would no longer work because the assumption that anomalous nodes were close with each other could not hold. Moreover, the nature of class imbalance in anomaly detection tasks brings great challenges to reduce the prediction error. In this work, we propose a novel loss function to train GNNs for anomaly-detectable node representations. It evaluates node similarity using global grouping patterns discovered from graph mining algorithms. It can automatically adjust margins for minority classes based on data distribution. Theoretically, we prove that the prediction error is bounded given the proposed loss function. We empirically investigate the GNN effectiveness of different loss variants based on different algorithms. Experiments on two real-world datasets show that they perform significantly better than RW-based loss for graph anomaly detection.

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Index Terms

Error-Bounded Graph Anomaly Loss for GNNs
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Anomaly detection
    2. Machine learning approaches
      1. Learning latent representations
      2. Neural networks

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cover image ACM Conferences

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

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Published: 19 October 2020

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Feng WWang LHooi BNg SLiu S(2024)Interrelated Dense Pattern Detection in Multilayer NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339868336:11(6462-6476)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3398683
Lai YWaniek MLi LWu JZhu YMichalak TRahwan TZhou K(2024)Coupled-Space Attacks Against Random-Walk-Based Anomaly DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.346815619(9315-9329)Online publication date: 2024
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