Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …

Abstract We present the Open Graph Benchmark (OGB), a diverse set of challenging and
realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine …

The history of artificial intelligence (AI) has witnessed the significant impact of high-quality
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …

Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

Graph neural networks (GNNs) have shown high potential for a variety of real-world,
challenging applications, but one of the major obstacles in GNN research is the lack of large …

Graph databases (GDBs) enable processing and analysis of unstructured, complex, rich,
and usually vast graph datasets. Despite the large significance of GDBs in both academia …

Graph-structured data, prevalent in domains ranging from social networks to biochemical
analysis, serve as the foundation for diverse real-world systems. While graph neural …

Machine learning over graphs have been emerging as powerful learning tools for graph
data. However, it is challenging for industrial communities to leverage the techniques, such …

Enabling effective and efficient machine learning (ML) over large-scale graph data (eg,
graphs with billions of edges) can have a great impact on both industrial and scientific …

In this comprehensive research paper, we delve into the intricate integration of Graph
Databases within Master Data Management (MDM) systems, aiming to revolutionize the …