This work aims to address the challenge of precise and accurate anomaly detection in dynamic graph networks. It uses a graph-based diffusion technique to sample a fixed-size, yet cross-coupled, ...

[paper] [code] [Li2024] PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection ... [paper] [code] [Tao2025] Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly ...

Angelo Libertucci, global head of industry for telecom at Google Cloud, discusses the hyperscaler’s recently unveiled ...

This a Tensorflow implementation of the ComGA algorithm, which designs a tailored deep graph convolutional network (tGCN) to capture local, global and structure anomalies for anomaly detection on ...

Expanded Application Areas: The versatility of AI applications is expanding rapidly, covering a broad spectrum of sectors. From autonomous vehicles and smart homes to predictive analytics in ...

This work proposes a model to detect collusion in stock markets through the application of graph mining and anomaly detection. Creation of investor graphs denoting the relationships between investors ...

PepGen leverages a graph-based approach to improve the detection of hidden protein variants in a computationally efficient ...

Power, Protection, and Intelligence Market OutlookLuton, Bedfordshire, United Kingdom, June 19, 2025 (GLOBE NEWSWIRE) -- As the world accelerates toward electrification, the electric vehicle (EV) ...

Highlights:Three drillholes along conductive trend HL-04 have returned nickel-bearing sulphide mineralization with grades up ...

Stochastic Block Model: A statistical model for generating random graphs wherein nodes are divided into groups, and edge probabilities depend on group membership. Community Detection: The process ...