Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph
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Abstract (arXiv)
Anomaly detection on attributed graphs is a crucial topic for its practical application. Existing methods suffer from semantic mixture and imbalance issue because they mainly focus on anomaly discrimination, ignoring representation learning. It conflicts with the assortativity assumption that anomalous nodes commonly connect with normal nodes directly. Additionally, there are far fewer anomalous nodes than normal nodes, indicating a long-tailed data distribution. To address these challenges, a unique algorithm,Decoupled Self-supervised Learning forAnomalyDetection (DSLAD), is proposed in this paper. DSLAD is a self-supervised method with anomaly discrimination and representation learning decoupled for anomaly detection. DSLAD employs bilinear pooling and masked autoencoder as the anomaly discriminators. By decoupling anomaly discrimination and representation learning, a balanced feature space is constructed, in which nodes are more semantically discriminative, as well as imbalance issue can be resolved. Experiments conducted on various six benchmark datasets reveal the effectiveness of DSLAD.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 27 of its 50 references that are in the map. First posted on arXiv 11 Apr 2023.
Builds on
27 of 50 references.
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15 more
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