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Found 4 papers, 2 papers with code
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework
Although federated learning offers a promising solution, the prevalent non-IID problems and high communication costs present significant challenges, particularly pronounced in collaborations with graph data distributed among different participants.
Self-Discriminative Modeling for Anomalous Graph Detection
Under the framework, we provide three algorithms with different computational efficiencies and stabilities for anomalous graph detection.
Deep Orthogonal Hypersphere Compression for Anomaly Detection
Many well-known and effective anomaly detection methods assume that a reasonable decision boundary has a hypersphere shape, which however is difficult to obtain in practice and is not sufficiently compact, especially when the data are in high-dimensional spaces.
Efficient Deep Embedded Subspace Clustering
The proposed method is out of the self-expressive framework, scales to the sample size linearly, and is applicable to arbitrarily large datasets and online clustering scenarios.
