Ministry of Testing

Testing data quality effectively

In some ways, data and its quality can seem strange to people used to assessing the quality of software. There’s often no observable behaviour to check and little in the way of structure to help you ...
IEEE

Graph Convolution for Large-Scale Graph Node Classification Task Based on Spatial and Frequency Domain Fusion

Abstract: In recent years, Graph Neural Networks (GNNs) have achieved significant success in graph-based tasks. However, they still face challenges in complex scenarios, particularly in integrating ...
AlphaGalileo

A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications

The 2024 Nobel Prize in Chemistry was recently granted to David Baker, Demis Hassabis and John M. Jumper, renowned for their pioneering works in protein design. In addition, Nature has recently ...
GitHub

CGBridge: Bridging Code Graphs and Large Language Models for Better Structure-Aware Code Understanding

CGBridge is a novel framework designed to enhance the code understanding capabilities of Large Language Models (LLMs) by integrating rich structural information from code graphs. Our approach follows ...
GitHub

The Connected Data Knowledge Graph Challenge

The Connected Data platform provides a Community, Events, and Thought Leadership for those who use the Relationships, Meaning and Context in Data to achieve great things. We've been Connecting Data, ...
IEEE

Fusion Graph Structure Learning-Based Multivariate Time Series Anomaly Detection With Structured Prior Knowledge

Abstract: Multivariate time series anomaly detection (MTSAD) plays a crucial role in the Internet of Things (IoT) to identify device malfunction or system attacks. Graph neural networks (GNN) are ...