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Unifying unsupervised graph-level anomaly detection and out-of-distribution detection: A benchmark
To build safe and reliable graph machine learning systems, unsupervised graph-level
anomaly detection (GLAD) and unsupervised graph-level out-of-distribution (OOD) detection …
anomaly detection (GLAD) and unsupervised graph-level out-of-distribution (OOD) detection …
GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generation
Despite graph neural networks'(GNNs) great success in modelling graph-structured data,
out-of-distribution (OOD) test instances still pose a great challenge for current GNNs. One of …
out-of-distribution (OOD) test instances still pose a great challenge for current GNNs. One of …
From unsupervised to few-shot graph anomaly detection: A multi-scale contrastive learning approach
Anomaly detection from graph data is an important data mining task in many applications
such as social networks, finance, and e-commerce. Existing efforts in graph anomaly …
such as social networks, finance, and e-commerce. Existing efforts in graph anomaly …
Rethinking independent cross-entropy loss for graph-structured data
Graph neural networks (GNNs) have exhibited prominent performance in learning graph-
structured data. Considering node classification task, based on the iid assumption among …
structured data. Considering node classification task, based on the iid assumption among …
Pioneering Reliable Assessment in Text-to-Image Knowledge Editing: Leveraging a Fine-Grained Dataset and an Innovative Criterion
During pre-training, the Text-to-Image (T2I) diffusion models encode factual knowledge into
their parameters. These parameterized facts enable realistic image generation, but they may …
their parameters. These parameterized facts enable realistic image generation, but they may …
Out-of-Distribution Detection on Graphs: A Survey
Graph machine learning has witnessed rapid growth, driving advancements across diverse
domains. However, the in-distribution assumption, where training and testing data share the …
domains. However, the in-distribution assumption, where training and testing data share the …