Graph neural networks (GNNs) have achieved tremendous success on multiple graph-based learning tasks by fusing network structure and node features. Modern GNN models are built upon iterative aggregation of neighbor's/proximity features by message passing. Its prediction performance has been shown to be strongly bounded by assortative mixing in the graph, a key property wherein nodes with similar attributes mix/connect with each other. We observe that real world networks exhibit heterogeneous or diverse mixing patterns and the conventional global measurement of assortativity, such as global assortativity coefficient, may not be a representative statistic in quantifying this mixing. We adopt a generalized concept, node-level assortativity, one that is based at the node level to better represent the diverse patterns and accurately quantify the learnability of GNNs. We find that the prediction performance of a wide range of GNN models is highly correlated with the node level assortativity. To break this limit, in this work, we focus on transforming the input graph into a computation graph which contains both proximity and structural information as distinct type of edges. The resulted multi-relational graph has an enhanced level of assortativity and, more importantly, preserves rich information from the original graph. We then propose to run GNNs on this computation graph and show that adaptively choosing between structure and proximity leads to improved performance under diverse mixing. Empirically, we show the benefits of adopting our transformation framework for semi-supervised node classification task on a variety of real world graph learning benchmarks.

中文翻译：

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通过提高具有局部混合模式的图的可配性来打破图神经网络的限制

图神经网络 (GNN) 通过融合网络结构和节点特征，在多个基于图的学​​习任务上取得了巨大成功。现代 GNN 模型建立在通过消息传递迭代聚合邻居/邻近特征的基础上。其预测性能已被证明受到图中分类混合的强烈限制，这是一个关键属性，其中具有相似属性的节点相互混合/连接。我们观察到现实世界的网络表现出异质或多样化的混合模式，并且传统的全局分类测量，例如全局分类系数，可能不是量化这种混合的代表性统计数据。我们采用一个广义的概念，节点级的分类，一种基于节点级别的方法，可以更好地表示不同的模式并准确量化 GNN 的可学习性。我们发现各种 GNN 模型的预测性能与节点级别的分类高度相关。为了打破这个限制，在这项工作中，我们专注于将输入图转换为一个计算图，其中包含作为不同类型边的邻近度和结构信息。由此产生的多关系图具有更高的分类水平，更重要的是，保留了原始图中的丰富信息。然后，我们建议在此计算图上运行 GNN，并表明在结构和邻近度之间进行自适应选择可以提高不同混合下的性能。根据经验，