Graph-level Anomaly Detection via Hierarchical Memory Networks
- URL: http://arxiv.org/abs/2307.00755v1
- Date: Mon, 3 Jul 2023 04:57:53 GMT
- Title: Graph-level Anomaly Detection via Hierarchical Memory Networks
- Authors: Chaoxi Niu, Guansong Pang, Ling Chen
- Abstract summary: We propose a novel approach called Hierarchical Memory Networks (HimNet)
HimNet learns hierarchical memory modules -- node and graph memory modules -- via a graph autoencoder network architecture.
The two modules are jointly optimized to detect both locally- and globally-anomalous graphs.
- Score: 19.217808857527743
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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