Open-Set Graph Anomaly Detection via Normal Structure Regularisation
CoRR(2023)
摘要
This paper considers an important Graph Anomaly Detection (GAD) task, namely
open-set GAD, which aims to detect anomalous nodes using a small number of
labelled training normal and anomaly nodes (known as seen anomalies) that
cannot illustrate all possible inference-time abnormalities. The availability
of that labelled data provides crucial prior knowledge about abnormalities for
GAD models, enabling substantially reduced detection errors. However, current
methods tend to over-emphasise fitting the seen anomalies, leading to a weak
generalisation ability to detect unseen anomalies, i.e., those that are not
illustrated by the labelled anomaly nodes. Further, they were introduced to
handle Euclidean data, failing to effectively capture important non-Euclidean
features for GAD. In this work, we propose a novel open-set GAD approach,
namely Normal Structure Regularisation (NSReg), to achieve generalised
detection ability to unseen anomalies, while maintaining its effectiveness on
detecting seen anomalies. The key idea in NSReg is to introduce a
regularisation term that enforces the learning of compact, semantically-rich
representations of normal nodes based on their structural relations to other
nodes. When being optimised with supervised anomaly detection losses, the
regularisation term helps incorporate strong normality into the modelling,
empowering the joint learning of both seen abnormality and normality of the
nodes, and thus, it effectively avoids the over emphasis on solely fitting the
seen anomalies during training. Extensive empirical results on six real-world
datasets demonstrate the superiority of our proposed NSReg for open-set GAD.
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