Abstract: Graph machine learning has been extensively studied in both academia and industry. Although booming with a vast number of emerging methods and techniques, most of the literature is built on ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

When business researchers analyze data, they often rely on assumptions to help make sense of what they find. But like anyone else, they can run into a whole lot of trouble if those assumptions turn ...

Abstract: Graph neural networks (GNNs) have shown promise in graph classification tasks, but they struggle to identify out-of-distribution (OOD) graphs often encountered in real-world scenarios, ...

Royalty-free licenses let you pay once to use copyrighted images and video clips in personal and commercial projects on an ongoing basis without requiring additional payments each time you use that ...

Recent market turmoil has led to a significant widening in credit spreads, with these at historically above-average levels, albeit far from prior peaks. Data by YCharts Wider credit spreads increase ...

A monthly food distribution at Normal West high school often included staples such as potatoes and apples. A food pantry that serves hundreds of Bloomington-Normal residents each month is ending. One ...

Generate RDF graphs compliant with VEO. Validate and extend ontology-based models. Interface with SOSA, TIME, and GEO vocabularies. Simplify integration with machine learning workflows and early ...

In this valuable study, the authors analyze droplet size distributions of multiple protein condensates and their fit to a scaling ansatz, highlighting that they exhibit features of first- and ...

Sept. 18, 1924: Fifteen representative businessmen assembled at the Association of Commerce yesterday afternoon to meet representatives of the Hockenbury Hotel Co., of Harrisburg, Pennsylvania, which ...