Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …

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 …

Pretraining a language model (LM) on text has been shown to help various downstream
NLP tasks. Recent works show that a knowledge graph (KG) can complement text data …

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 …

Knowledge graph (KG) plays an increasingly important role in recommender systems. A
recent technical trend is to develop end-to-end models founded on graph neural networks …

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 …

Drug discovery is the process of new drug identification. This process is driven by the
increasing data from existing chemical libraries and data banks. The knowledge graph is …

Drug repurposing or repositioning is a technique whereby existing drugs are used to treat
emerging and challenging diseases, including COVID-19. Drug repurposing has become a …

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 …

Knowledge graph completion (KGC) aims to reason over known facts and infer the missing
links. Text-based methods such as KGBERT (Yao et al., 2019) learn entity representations …