A maturing area of research in deep learning is the development of
architectures that can learn explicit representations of relational features.
In this paper, we focus on the problem of learning representations of
hierarchical relations, proposing an architectural framework we call
"relational convolutional networks". Given a sequence of objects, a
"multi-dimensional inner product relation" module produces a relation tensor
describing all pairwise relations. A "relational convolution" layer then
transforms the relation tensor into a sequence of new objects, each describing
the relations within some group of objects at the previous layer. Graphlet
filters, analogous to filters in convolutional neural networks, represent a
template of relations against which the relation tensor is compared at each
grouping. Repeating this yields representations of higher-order, hierarchical
relations. We present the motivation and details of the architecture, together
with a set of experiments to demonstrate how relational convolutional networks
can provide an effective framework for modeling relational tasks that have
hierarchical structure.

深度学习中一个日益成熟的研究领域是开发能够学习关系特征的显式表示的架构。本文提出了一个叫做 “关系卷积网络” 的架构框架，重点研究了学习分层关系表示的问题，并通过一系列实验展示了如何利用关系卷积网络提供有效的建模方法来处理具有层次结构的关系任务。