Graphs have a superior ability to represent relational data, like chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …

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 …

Graph neural networks (GNNs) have attracted tremendous attention from the graph learning
community in recent years. It has been widely adopted in various real-world applications …

Graph-structured data arise in many scenarios. A fundamental problem is to quantify the
similarities of graphs for tasks such as classification. R-convolution graph kernels are …

In the era of big data, graph data has attracted considerable attention, ranging from social
networks, biological networks to recommendation systems. For example, in social network …

A wide range of machine learning problems involve handling graph-structured data. Existing
machine learning approaches for graphs, however, often imply computing expensive graph …

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

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 …

This paper presents a general graph representation learning framework called DeepGL for
learning deep node and edge representations from large (attributed) graphs. In particular …

Neural networks are typically designed to deal with data in tensor forms. In this paper, we
propose a novel neural network architecture accepting graphs of arbitrary structure. Given a …