Graph Anomaly Detection

31 papers with code • 0 benchmarks • 0 datasets

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Use these libraries to find Graph Anomaly Detection models and implementations
2 papers

Most implemented papers

Energy Transformer

bhoov/energy-transformer-jax 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

WangXuhongCN/OCGNN 22 Feb 2020

Since traditional anomaly detection methods are stable, robust and easy to use, it is vitally important to generalize them to graph data.

GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction

graph-com/gad-nr 2 Jun 2023

Graph Anomaly Detection (GAD) is a technique used to identify abnormal nodes within graphs, finding applications in network security, fraud detection, social media spam detection, and various other domains.

Coupled-Space Attacks against Random-Walk-based Anomaly Detection

yuni-lai/coupledattackrw 26 Jul 2023

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 21 Aug 2023

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 20 Oct 2020

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 14 Jun 2021

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 6 Jul 2021

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 23 Aug 2021

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 31 May 2022

Graph Neural Networks (GNNs) are widely applied for graph anomaly detection.