Graph representation learning is a key technology for processing graph-structured data. Graph convolutional networks (GCNs), as a type of currently emerging and commonly used model for graph representation learning, have achieved significant performance improvement. However, GCNs acquire node representations mainly through aggregating their neighbor information, largely ignoring the community structure which is one of the most important feature of the graph. In this paper, we propose a novel method called Community Enhanced Graph Convolutional Networks (CE-GCN), which integrates both neighborhood and community information to learn node representations. Specifically, the neighborhood information of nodes is aggregated by a graph convolutional network. The community information of nodes is calculated by a modularity constraint. Finally, we incorporate the modularity constraint into the graph convolutional network, and then form a unified model framework. Experimental results on five real-world network datasets demonstrate that CE-GCN significantly outperforms state-of-the-art methods.

图表示学习是处理图结构数据的关键技术。图卷积网络（GCN）作为一种新出现的，常用的图表示学习模型，已经取得了显着的性能提升。但是，GCN主要通过聚集其邻居信息来获取节点表示，而很大程度上忽略了社区结构，这是该图的最重要特征之一。在本文中，我们提出了一种称为社区增强图卷积网络（CE-GCN）的新方法，该方法融合了邻域和社区信息以学习节点表示。具体地，节点的邻域信息由图卷积网络聚合。节点的社区信息是通过模块化约束来计算的。最后，我们将模块化约束条件合并到图卷积网络中，然后形成一个统一的模型框架。在五个真实网络数据集上的实验结果表明，CE-GCN明显优于最新方法。