arXiv Machine Learning
Jun 2
Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection
arXiv:2606. 00304v1 Announce Type: new Abstract: Graph anomaly detection methods aim to distinguish anomalous nodes.
A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction
arXiv:2606. 12673v1 Announce Type: cross Abstract: Cross-domain graph anomaly detection (GAD) aims to identify abnormal nodes in unseen target graphs, showing strong potential in real-world applications with heterogeneous graph data.
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arXiv Machine Learning
Jul 31
ARES: Anomaly Recognition Model For Edge Streams
arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.
arXiv Machine Learning
Jun 9
Normality Calibration in Semi-supervised Graph Anomaly Detection
arXiv:2510. 02014v3 Announce Type: replace Abstract: Graph anomaly detection (GAD) has attracted growing interest for its crucial ability to uncover irregular patterns in broad applications.
arXiv Machine Learning
Jun 8
Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection
arXiv:2505. 21285v5 Announce Type: replace Abstract: This work proposes a framework LGKDE that learns kernel density estimation for graphs.
arXiv AI
Jul 13
AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders
arXiv:2511. 17113v3 Announce Type: replace-cross Abstract: Network Intrusion Detection Systems (NIDS) are essential tools for detecting network attacks and intrusions.
arXiv AI
Aug 12
ProTAGAD: A Foundation Model for TAG Anomaly Detection with Decoupled Topological and Textual Prototypes
arXiv:2608. 10699v1 Announce Type: cross Abstract: Text-Attributed Graphs (TAGs), endowed with abundant textual content along with topological structures, have emerged as a versatile backbone for real-world anomaly detection spanning large language model security, social network moderation, and cyber threat identification.
arXiv Machine Learning
1d ago
Beat the Counter First: A Baseline for Temporal-Graph Anomaly Detectors
arXiv:2608. 15965v1 Announce Type: new Abstract: Progress in streaming, edge-level graph anomaly detection (GAD) has been marked by increasingly elaborate architectures, from count-min-sketch chi square tests to memory-augmented attention networks.
arXiv AI
Jun 2
Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision
arXiv:2602. 20019v2 Announce Type: replace-cross Abstract: Dynamic graph anomaly detection is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies.
arXiv Machine Learning
Jun 18
Towards Anomaly Detection on Relational Data
arXiv:2606. 18621v1 Announce Type: new Abstract: Relational databases are widely used for managing structured data in real-world systems.
arXiv Machine Learning
Jul 28
Unsupervised Graph Representation Learning with Complementary View Alignment
arXiv:2607. 24338v1 Announce Type: new Abstract: Unsupervised graph representation learning aims to derive meaningful node embeddings by capturing both structural and attribute information without relying on labeled data.
arXiv AI
Jul 22
Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection
arXiv:2505. 18934v2 Announce Type: replace-cross Abstract: Graph Anomaly Detection (GAD) in heterogeneous networks presents unique challenges due to node and edge heterogeneity.
Hugging Face Trending Papers
Jun 17
Towards Anomaly Detection on Relational Data
Relational databases are widely used for managing structured data in real-world systems. Detecting anomalies from such relational data is crucial for identifying fraud, risks, and abnormal behaviors, yet remains under-explored.