Graph Anomaly Detection
29 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Graph Anomaly Detection
Most implemented papers
Energy Transformer
bhoov/energy-transformer-jax
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NeurIPS 2023
Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory.
One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks
Since traditional anomaly detection methods are stable, robust and easy to use, it is vitally important to generalize them to graph data.
Coupled-Space Attacks against Random-Walk-based Anomaly Detection
In addition, we conduct transfer attack experiments in a black-box setting, which show that our feature attack significantly decreases the anomaly scores of target nodes.
Label-based Graph Augmentation with Metapath for Graph Anomaly Detection
missinghwan/mgad
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To further efficiently exploit context information from metapath-based anomaly subgraph, we present a new framework, Metapath-based Graph Anomaly Detection (MGAD), incorporating GCN layers in both the dual-encoders and decoders to efficiently propagate context information between abnormal and normal nodes.
Action Sequence Augmentation for Early Graph-based Anomaly Detection
dm2-nd/eland
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With Eland, anomaly detection performance at an earlier stage is better than non-augmented methods that need significantly more observed data by up to 15% on the Area under the ROC curve.
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning
XiaoxiaoMa-MQ/Awesome-Deep-Graph-Anomaly-Detection
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In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection.
On Generalization of Graph Autoencoders with Adversarial Training
Juintin/GAE-AT
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Adversarial training is an approach for increasing model's resilience against adversarial perturbations.
Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection
kimmeen/sl-gad
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While the generative attribute regression module allows us to capture the anomalies in the attribute space, the multi-view contrastive learning module can exploit richer structure information from multiple subgraphs, thus abling to capture the anomalies in the structure space, mixing of structure, and attribute information.
Rethinking Graph Neural Networks for Anomaly Detection
squareroot3/rethinking-anomaly-detection
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Graph Neural Networks (GNNs) are widely applied for graph anomaly detection.
AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach
YangSJ2019/AHEAD
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In light of this problem, we propose AHEAD: a heterogeneity-aware unsupervised graph anomaly detection approach based on the encoder-decoder framework.
