Graph-structured data are ubiquitous. However, graphs encode diverse types of information and thus play different roles in data representation. In this paper, we distinguish the \textit{representational} and the \textit{correlational} roles played by the graphs in node-level prediction tasks, and we investigate how Graph Neural Network (GNN) models can effectively leverage both types of information. Conceptually, the representational information provides guidance for the model to construct better node features; while the correlational information indicates the correlation between node outcomes conditional on node features. Through a simulation study, we find that many popular GNN models are incapable of effectively utilizing the correlational information. By leveraging the idea of the copula, a principled way to describe the dependence among multivariate random variables, we offer a general solution. The proposed Copula Graph Neural Network (CopulaGNN) can take a wide range of GNN models as base models and utilize both representational and correlational information stored in the graphs. Experimental results on two types of regression tasks verify the effectiveness of the proposed method.

本文研究如何有效地利用两种类型的信息来提高节点预测任务的性能，即图的表示信息和相关信息，并通过提出基于Copula Graph Neural Network的模型解决了GNN模型无法充分利用相关信息的问题。实验结果表明，该方法在两种类型的回归任务上具有良好的效果。

CopulaGNN：在图神经网络中融合图的代表性与相关性角色