Graph convolutional networks (GCN) with one or two hidden layers have been widely used
in handling graph data that are prevalent in various disciplines. Many studies showed that …

Graph neural networks, which generalize deep neural network models to graph structured
data, have attracted increasing attention in recent years. They usually learn node …

Graph Neural Nets (GNNs) have received increasing attentions, partially due to their
superior performance in many node and graph classification tasks. However, there is a lack …

Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks,
are receiving extensive attention for their powerful capability in learning node …

Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement
in deep learning field and their applications are quickly spreading in multi-cross-domains …

Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-
structured data. Recently, GCNs and subsequent variants have shown superior performance …

Graph kernels have been successfully applied to many graph classification problems.
Typically, a kernel is first designed, and then an SVM classifier is trained based on the …

Graph convolutional network (GCN) is an effective neural network model for graph
representation learning. However, standard GCN suffers from three main limitations:(1) most …

We introduce a family of multilayer graph kernels and establish new links between graph
convolutional neural networks and kernel methods. Our approach generalizes convolutional …

Graph data widely exist in many high-impact applications. Inspired by the success of deep
learning in grid-structured data, graph neural network models have been proposed to learn …