Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2023

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.

Keywords

Anomaly detection, Auto enc, oders, Fine grained, Graph neural networks, Graph-level anomaly detection, Hierarchical memory, Learn+, Memory modules, Memory network, Node attribute

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Turin, Italy, 2023 September 18-22

Volume

14169

First Page

201

Last Page

218

ISBN

9783031434129

Identifier

10.1007/978-3-031-43412-9_12

Publisher

Springer Nature Switzerland

City or Country

London

Copyright Owner and License

Authors

Additional URL

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

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