Graph-Level Anomaly Detection via Hierarchical Memory Networks
Date
2023
Authors
Niu, C.
Pang, G.
Chen, L.
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Conference paper
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Proceedings, Part I of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023), as published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023, vol.14169 LNAI, pp.201-218
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Chaoxi Niu, Guansong Pang, and Ling Chen
Conference Name
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) (18 Sep 2023 - 22 Sep 2023 : Turin, Italy)
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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© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023