A graph is one of important mathematical tools to describe ubiquitous relations. In the
classical graph theory and some applications, graphs are generally provided in advance, or …

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 are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …

In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …

Graphs are widely used to describe real-world objects and their interactions. Graph Neural
Networks (GNNs) as a de facto model for analyzing graphstructured data, are highly …

Recently, there has been an increasing interest in (supervised) learning with graph data,
especially using graph neural networks. However, the development of meaningful …

Graph neural networks have emerged as a leading architecture for many graph-level tasks,
such as graph classification and graph generation. As an essential component of the …

Graph classification, which aims to identify the category labels of graphs, plays a significant
role in drug classification, toxicity detection, protein analysis etc. However, the limitation of …

There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-
structured data. However, training GNNs requires abundant task-specific labeled data …