Abstract Graph representation learning is of paramount importance for a variety of graph analytical tasks, ranging from node classification to community detection. Recently, graph convolutional networks (GCNs) have been successfully applied for graph representation learning. These GCNs generate node representation by aggregating features from the neighborhoods, which follows the “neighborhood aggregation” scheme. In spite of having achieved promising performance on various tasks, existing GCN-based models have difficulty in well capturing complicated non-linearity of graph data. In this paper, we first theoretically prove that coefficients of the neighborhood interacting terms are relatively small in current models, which explains why GCNs barely outperforms linear models. Then, in order to better capture the complicated non-linearity of graph data, we present a novel GraphAIR framework which models the neighborhood interaction in addition to neighborhood aggregation. Comprehensive experiments conducted on benchmark tasks including node classification and link prediction using public datasets demonstrate the effectiveness of the proposed method.

中文翻译：

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GraphAIR：具有邻域聚合和交互的图表示学习

摘要 图表示学习对于从节点分类到社区检测的各种图分析任务都至关重要。最近，图卷积网络（GCN）已成功应用于图表示学习。这些 GCN 通过聚合来自邻域的特征来生成节点表示，这遵循“邻域聚合”方案。尽管在各种任务上取得了可喜的性能，但现有的基于 GCN 的模型难以很好地捕捉图数据的复杂非线性。在本文中，我们首先从理论上证明了当前模型中邻域交互项的系数相对较小，这解释了为什么 GCN 几乎没有优于线性模型。然后，为了更好地捕捉图数据的复杂非线性，我们提出了一种新颖的 GraphAIR 框架，除了邻域聚合之外，它还对邻域交互进行建模。使用公共数据集对包括节点分类和链接预测在内的基准任务进行的综合实验证明了所提出方法的有效性。