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

Abstract Graph Convolutional Networks (GCNs) and their variants have experienced
significant attention and have become the de facto methods for learning graph …

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

Recent advances in graph convolutional networks (GCNs), which mainly focus on how to
exploit information from different hops of neighbors in an efficient way, have brought …

Graph convolutional network (GCN) with the powerful capacity to explore graph-structural
data has gained noticeable success in recent years. Nonetheless, most of the existing GCN …

Graph Convolutional Networks (GCNs) have been successfully applied to analyze non-grid
data, where the classical convolutional neural networks (CNNs) cannot be directly used …

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 networks (GCNs) have received significant attention from various
research fields due to the excellent performance in learning graph representations. Although …

Graph neural networks (GNNs) are a popular class of machine learning models that have
been successfully applied to a range of problems. Their major advantage lies in their ability …