CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks

international conference on learning representations, 2020.

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We distinguish the representational and the correlational information encoded by the graphs in node-level prediction tasks, and propose a novel Copula Graph Neural Network to effectively leverage both information.

Abstract:

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 ...More

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