Graph is an important data representation which appears in a wide diversity of real-world
scenarios. Effective graph analytics provides users a deeper understanding of what is …

With the widespread use of information technologies, information networks are becoming
increasingly popular to capture complex relationships across various disciplines, such as …

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 …

Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as
in information retrieval, natural language processing, recommendation systems, etc …

The goal of graph representation learning is to embed each vertex in a graph into a low-
dimensional vector space. Existing graph representation learning methods can be classified …

Recommender systems (RSs) have been the most important technology for increasing the
business in Taobao, the largest online consumer-to-consumer (C2C) platform in China …

Research on graph representation learning has received great attention in recent years
since most data in real-world applications come in the form of graphs. High-dimensional …

In online social networks people often express attitudes towards others, which forms
massive sentiment links among users. Predicting the sign of sentiment links is a fundamental …

Graph representation learning has been extensively studied in recent years, in which
sampling is a critical point. Prior arts usually focus on sampling positive node pairs, while the …

Deep candidate generation (DCG) that narrows down the collection of relevant items from
billions to hundreds via representation learning has become prevalent in industrial …