Sensors, Free Full-Text

Por um escritor misterioso

Last updated 03 abril 2025

Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. Graph representation learning aims to map graph entities to low-dimensional vectors while preserving graph structure and entity relationships. Over the decades, many models have been proposed for graph representation learning. This paper aims to show a comprehensive picture of graph representation learning models, including traditional and state-of-the-art models on various graphs in different geometric spaces. First, we begin with five types of graph embedding models: graph kernels, matrix factorization models, shallow models, deep-learning models, and non-Euclidean models. In addition, we also discuss graph transformer models and Gaussian embedding models. Second, we present practical applications of graph embedding models, from constructing graphs for specific domains to applying models to solve tasks. Finally, we discuss challenges for existing models and future research directions in detail. As a result, this paper provides a structured overview of the diversity of graph embedding models.

100us PNP Output Easy Setting Digital Fiber Optic Amplifier Position Sensor

Omnicomm LLS 4 Fuel Sensor

Handbook of modern sensors : physics, designs, and applications : Fraden, Jacob : Free Download, Borrow, and Streaming : Internet Archive

Advanced Healthcare Materials: Early View

PDF) Sensors, Free Full-Text

FST800-2100 3 wire 0-10V 500 psi Chiller Pressure Transducer for Water Pressure Test - China pressure monitoring transducer, chiller pressure transducer 500 psi