Self-supervised anomaly detection in computer vision and beyond: A survey and outlook
Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity,
finance, and healthcare, by identifying patterns or events that deviate from normal …
finance, and healthcare, by identifying patterns or events that deviate from normal …
Correlation-aware spatial–temporal graph learning for multivariate time-series anomaly detection
Multivariate time-series anomaly detection is critically important in many applications,
including retail, transportation, power grid, and water treatment plants. Existing approaches …
including retail, transportation, power grid, and water treatment plants. Existing approaches …
Self-supervised anomaly detection: A survey and outlook
Over the past few years, anomaly detection, a subfield of machine learning that is mainly
concerned with the detection of rare events, witnessed an immense improvement following …
concerned with the detection of rare events, witnessed an immense improvement following …
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 …
Projective ranking-based gnn evasion attacks
Graph neural networks (GNNs) offer promising learning methods for graph-related tasks.
However, GNNs are at risk of adversarial attacks. Two primary limitations of the current …
However, GNNs are at risk of adversarial attacks. Two primary limitations of the current …
DualGAD: Dual-bootstrapped self-supervised learning for graph anomaly detection
H Tang, X Liang, J Wang, S Zhang - Information Sciences, 2024 - Elsevier
Graph anomaly detection (GAD) is an emerging and essential research field for discovering
anomalous individuals (eg, nodes or edges) that deviate significantly from the normal …
anomalous individuals (eg, nodes or edges) that deviate significantly from the normal …
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection
F Xu, N Wang, X Wen, M Gao, C Guo… - 2023 IEEE 29th …, 2023 - ieeexplore.ieee.org
Graph anomaly detection plays a crucial role in identifying exceptional instances in graph
data that deviate significantly from the majority. It has gained substantial attention in various …
data that deviate significantly from the majority. It has gained substantial attention in various …
GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs
K Reynisson, M Schreyer, D Borth - arXiv preprint arXiv:2407.12440, 2024 - arxiv.org
Credit card fraud has significant implications at both an individual and societal level, making
effective prevention essential. Current methods rely heavily on feature engineering and …
effective prevention essential. Current methods rely heavily on feature engineering and …
Adaptive multi-layer contrastive graph neural networks
S Shi, P Xie, X Luo, K Qiao, L Wang, J Chen… - Neural Processing …, 2023 - Springer
Inspired by recent success of graph contrastive learning methods, we propose a self-
supervised learning framework for Graph Neural Networks (GNNs) named Adaptive Multi …
supervised learning framework for Graph Neural Networks (GNNs) named Adaptive Multi …
基于深度学习的属性图异常检测综述.
张伊扬， 钱育蓉， 陶文彬， 冷洪勇… - Journal of Computer …, 2022 - search.ebscohost.com
异常检测一直以来都是数据挖掘领域的研究热点之一, 其任务是在海量数据中识别罕见的观测
对象. 随着图数据挖掘的发展, 属性图异常检测在各个领域广受关注. 然而 …
对象. 随着图数据挖掘的发展, 属性图异常检测在各个领域广受关注. 然而 …