In this draft paper, we introduce a novel architecture for graph networks which is equivariant to the Euclidean group in $n$-dimensions. The model is designed to work with graph networks in their general form and can be shown to include particular variants as special cases. Thanks to its equivariance properties, we expect the proposed model to be more data efficient with respect to classical graph architectures and also intrinsically equipped with a better inductive bias. We defer investigating this matter to future work.

翻译：在本文件草案中,我们引入了一个用于图形网络的新结构,该结构对欧几里德集团来说是等式的,以美元为单位;该模型旨在与一般形式的图形网络合作,并可以显示将特定变体作为特例列入其中;由于其等式性质,我们期望拟议的模型在古典图形结构方面更有效率,而且内在地具备更好的感性偏差。我们把调查此事推迟到今后的工作。