Graph Convolutional Networks (GCNs) have gained great popularity in tackling
various analytics tasks on graph and network data. However, some recent studies
raise concerns about whether GCNs can optimally integrate node features and
topological structures in a complex graph with rich information. In this paper,
we first present an experimental investigation. Surprisingly, our experimental
results clearly show that the capability of the state-of-the-art GCNs in fusing
node features and topological structures is distant from optimal or even
satisfactory. The weakness may severely hinder the capability of GCNs in some
classification tasks, since GCNs may not be able to adaptively learn some deep
correlation information between topological structures and node features. Can
we remedy the weakness and design a new type of GCNs that can retain the
advantages of the state-of-the-art GCNs and, at the same time, enhance the
capability of fusing topological structures and node features substantially? We
tackle the challenge and propose an adaptive multi-channel graph convolutional
networks for semi-supervised classification (AM-GCN). The central idea is that
we extract the specific and common embeddings from node features, topological
structures, and their combinations simultaneously, and use the attention
mechanism to learn adaptive importance weights of the embeddings. Our extensive
experiments on benchmark data sets clearly show that AM-GCN extracts the most
correlated information from both node features and topological structures
substantially, and improves the classification accuracy with a clear margin.

本文提出了一种新型 GCNs 模型：自适应多通道图卷积网络 (AM-GCN)，以更好地融合和提取节点特征和拓扑结构之间的深度相关信息，并在半监督分类任务上取得了明显的精度提升。