Benchmarking Graph Neural Networks

Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. As the field grows, it becomes critical to identify key architectures and validate new ideas that generalize to larger, more complex datasets. Unfortunately, it has been increasingly difficult to gauge the effectiveness of new models in the absence of a standardized benchmark with consistent experimental settings. In this paper, we introduce a reproducible GNN benchmarking framework, with the facility for researchers to add new models conveniently for arbitrary datasets. We demonstrate the usefulness of our framework by presenting a principled investigation into the recent Weisfeiler-Lehman GNNs (WL-GNNs) compared to message passing-based graph convolutional networks (GCNs) for a variety of graph tasks, i.e. graph regression/classification and node/link prediction, with medium-scale datasets.Comment: Benchmarking framework on GitHub at https://github.com/graphdeeplearning/benchmarking-gnn

The Impact of Artificial Intelligence on the Future of Workforces in the European Union and the United States of America

Report on an economic study related to artificial intelligence: "This report is intended to highlight the economics behind AI-driven technological change with a particular focus on the institutional and policy decisions that will shape its future impact on the workforce. [...] Parts I and II of this report introduce AI and document its widespread adoption in the European Union and the United States; part III focuses on AI’s impact on labor; part IV contains case studies on hiring and logistics; part V concludes" (p. 3)

Digital Transformation in Healthcare

This book presents a collection of papers revealing the impact of advanced computation and instrumentation on healthcare. It highlights the increasing global trend driving innovation for a new era of multifunctional technologies for personalized digital healthcare. Moreover, it highlights that contemporary research on healthcare is performed on a multidisciplinary basis comprising computational engineering, biomedicine, biomedical engineering, electronic engineering, and automation engineering, among other areas