With the explosive growth of artificial intelligence (AI) and big data, it has become vitally
important to organize and represent the enormous volume of knowledge appropriately. As …

Introduction Knowledge graphs have proven to be promising systems of information storage
and retrieval. Due to the recent explosion of heterogeneous multimodal data sources …

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 …

Abstract Graph Neural Networks (GNNs) have shown great potential in the field of graph
representation learning. Standard GNNs define a local message-passing mechanism which …

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 …

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 …

In the 1940s, Claude Shannon developed the information theory focusing on quantifying the
maximum data rate that can be supported by a communication channel. Guided by this …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

There have been more than 2.2 million confirmed cases and over 120 000 deaths from the
human coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute …

Recently, knowledge graph embeddings (KGEs) have received significant attention, and
several software libraries have been developed for training and evaluation. While each of …