As a key enabling technology of Industry 4.0, Digital Twin (DT) has been widely applied to
various industrial domains covering different lifecycle phases of products and systems. To …

Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Implementing precision medicine hinges on the integration of omics data, such as
proteomics, into the clinical decision-making process, but the quantity and diversity of …

Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …

Link prediction is a very fundamental task on graphs. Inspired by traditional path-based
methods, in this paper we propose a general and flexible representation learning framework …

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 …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

As natural disasters are induced by geodynamic activities or abnormal changes in the
environment, geological hazards tend to wreak havoc on the environment and human …

Abstract Knowledge Graphs (KGs) have made a qualitative leap and effected a real
revolution in knowledge representation. This is leveraged by the underlying structure of the …