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Found 4 papers, 1 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 Graph-Level Orthogonal Hypersphere Compression for Anomaly Detection
Graph-level anomaly detection aims to identify anomalous graphs from a collection of graphs in an unsupervised manner.
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.
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