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Found 4 papers, 2 papers with code
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning
Finally, the model is refined with the only input of reliable normal nodes and learns a more accurate estimate of normality so that anomalous nodes can be more easily distinguished.
Hard Sample Aware Network for Contrastive Deep Graph Clustering
Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones.
Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View
However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance.
ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness
The average node-pair similarity can be regarded as the topology anomaly degree of nodes within substructures.
