… of graph neural networks applied to structural scenarios, non-structural scenarios and other
scenarios. In Section 9, we propose four open problems of graph neural networks as well as …

… The complexity of graph data has imposed significant challenges on the … for graph data
have emerged. In this article, we provide a comprehensive overview of graph neural networks (…

… , and financial networks, requires effective representation of their graph structure (Hamilton …
interest in Graph Neural Network (GNN) approaches for representation learning of graphs (Li …

… neural network model a graph neural network (GNN). It will be shown that the GNN is an
extension of both recursive neural networks … The model extends recursive neural networks since …

… range of topics in graph neural networks, by … -Graph Neural Networks (GNN):
Foundation, Frontiers and Applications [12], which can be easily accessed at https://graph-neural-networks.…

… We develop a series of graph neural networks (GNNs) with different depths in terms of …
graph neural network with depth of 0, which is approximated with a multi-layer perceptron network…

… based on graph neural networks. Specially, we propose a novel graph neural network GraphRec
… • We propose a novel graph neural network GraphRec, which can model graph data in …

… After an overview in Chapter 1, we introduce some basic knowledge of math and graph
theory in Chapter 2. We show the basics of neural networks in Chapter 3 and then give a brief …

… graph neural networks, and then refer to up to 327 relevant literatures to show the panorama
of the graph neural networks. … In order to drive the graph neural networks into a new stage, …

In the last few years, graph neural networks (GNNs) have become the standard toolkit for
analyzing and learning from data on graphs. This emerging field has witnessed an extensive …