Knowledge graph completion (KGC) is a hot topic in knowledge graph construction and
related applications, which aims to complete the structure of knowledge graph by predicting …

Link prediction is a paradigmatic problem in network science, which aims at estimating the
existence likelihoods of nonobserved links, based on known topology. After a brief …

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 …

Graph representation learning has emerged as a powerful technique for addressing real-
world problems. Various downstream graph learning tasks have benefited from its recent …

There has been a surge of recent interest in graph representation learning (GRL). GRL
methods have generally fallen into three main categories, based on the availability of …

Network embedding (or graph embedding) has been widely used in many real-world
applications. However, existing methods mainly focus on networks with single-typed …

With the rapid proliferation of scientific literature, versatile academic knowledge services
increasingly rely on comprehensive academic graph mining. Despite the availability of …

To capture higher-order structural features, most GNN-based algorithms learn node
representations incorporating k-hop neighbors' information. Due to the high time complexity …

Heterogeneous graph convolutional networks have gained great popularity in tackling
various network analytical tasks on heterogeneous network data, ranging from link …

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 …