Network representation learning (NRL) advances the conventional graph mining of social
networks, knowledge graphs, and complex biomedical and physics information networks …

We introduce a self-supervised approach for learning node and graph level representations
by contrasting structural views of graphs. We show that unlike visual representation learning …

Graph convolution is the core of most Graph Neural Networks (GNNs) and usually
approximated by message passing between direct (one-hop) neighbors. In this work, we …

In many domains where data are represented as graphs, learning a similarity metric among
graphs is considered a key problem, which can further facilitate various learning tasks, such …

Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph
analysis tasks such as node classification and link prediction. However, important …

Location Based Social Networks (LBSNs) have been widely used as a primary data source
to study the impact of mobility and social relationships on each other. Traditional …

Abstract Graph Neural Networks (GNNs) have shown exceptional performance in the task of
link prediction. Despite their effectiveness, the high latency brought by non-trivial …

We study the problem of large-scale network embedding, which aims to learn latent
representations for network mining applications. Previous research shows that 1) popular …

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 …

Graphs have been widely used to represent complex data in many applications, such as e-
commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data …