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Graph anomaly detection with graph neural networks: Current status and challenges
Graphs are used widely to model complex systems, and detecting anomalies in a graph is
an important task in the analysis of complex systems. Graph anomalies are patterns in a …
an important task in the analysis of complex systems. Graph anomalies are patterns in a …
Deep isolation forest for anomaly detection
Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector
in recent years due to its general effectiveness across different benchmarks and strong …
in recent years due to its general effectiveness across different benchmarks and strong …
Towards self-interpretable graph-level anomaly detection
Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable
dissimilarity compared to the majority in a collection. However, current works primarily focus …
dissimilarity compared to the majority in a collection. However, current works primarily focus …
Deep graph anomaly detection: A survey and new perspectives
Graph anomaly detection (GAD), which aims to identify unusual graph instances (nodes,
edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its …
edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its …
Good-d: On unsupervised graph out-of-distribution detection
Most existing deep learning models are trained based on the closed-world assumption,
where the test data is assumed to be drawn iid from the same distribution as the training …
where the test data is assumed to be drawn iid from the same distribution as the training …
Arc: a generalist graph anomaly detector with in-context learning
Graph anomaly detection (GAD), which aims to identify abnormal nodes that differ from the
majority within a graph, has garnered significant attention. However, current GAD methods …
majority within a graph, has garnered significant attention. However, current GAD methods …
Graphde: A generative framework for debiased learning and out-of-distribution detection on graphs
Despite the remarkable success of graph neural networks (GNNs) for graph representation
learning, they are generally built on the (unreliable) iid assumption across training and …
learning, they are generally built on the (unreliable) iid assumption across training and …
Dual-discriminative graph neural network for imbalanced graph-level anomaly detection
Graph-level anomaly detection aims to distinguish anomalous graphs in a graph dataset
from normal graphs. Anomalous graphs represent a very few but essential patterns in the …
from normal graphs. Anomalous graphs represent a very few but essential patterns in the …
Deep graph level anomaly detection with contrastive learning
Graph level anomaly detection (GLAD) aims to spot anomalous graphs that structure pattern
and feature information are different from most normal graphs in a graph set, which is rarely …
and feature information are different from most normal graphs in a graph set, which is rarely …
A data-centric framework to endow graph neural networks with out-of-distribution detection ability
Out-of-distribution (OOD) detection, which aims to identify OOD samples from in-distribution
(ID) ones in test time, has become an essential problem in machine learning. However …
(ID) ones in test time, has become an essential problem in machine learning. However …