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 …

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 …

Many real data come in the form of non-grid objects, ie graphs, from social networks to
molecules. Adaptation of deep learning from grid-alike data (eg images) to graphs has …

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

In the last decade or so, we have witnessed deep learning reinvigorating the machine
learning field. It has solved many problems in the domains of computer vision, speech …

Both node classification and link prediction are popular topics of supervised learning on the
graph data, but previous works seldom integrate them together to capture their …

Graph-structured data naturally appear in numerous application domains, ranging from
social analysis, bioinformatics to computer vision. The unique capability of graphs enables …

Deep learning has revolutionized many machine learning tasks in recent years, ranging
from image classification and video processing to speech recognition and natural language …

An increasing number of machine learning tasks require dealing with large graph datasets,
which capture rich and complex relation-ship among potentially billions of elements. Graph …

Graph classification aims to predict the class labels of graphs and has a wide range of
applications in many real-world domains. However, most of existing graph neural networks …