Multi-view graph embedding is aimed at learning low-dimensional representations of nodes that capture various relationships in a multi-view network, where each view represents a type of relationship among nodes. Multitudes of existing graph embedding approaches concentrate on single-view networks, that can only characterize one simple type of proximity relationships among objects. However, most of the real-world complex systems possess multiple types of relationships among entities. In this paper, a novel approach of graph embedding for multi-view networks is proposed, named Multi-view Graph Attention Networks (MGAT). We explore an attention-based architecture for learning node representations from each single view, the network parameters of which are constrained by a novel regularization term. In order to collaboratively integrate multiple types of relationships in different views, a view-focused attention method is explored to aggregate the view-wise node representations. We evaluate the proposed algorithm on several real-world datasets, and it demonstrates that the proposed approach outperforms existing state-of-the-art baselines.

多视图图嵌入旨在学习捕获多视图网络中各种关系的节点的低维表示，其中每个视图表示节点之间的一种关系。现有的大量图形嵌入方法都集中在单视图网络上，该网络只能描述对象之间一种简单类型的邻近关系。但是，大多数现实世界中的复杂系统在实体之间具有多种类型的关系。本文提出了一种新的多视图网络图嵌入方法，称为多视图图注意力网络（MGAT）。我们探索了一种基于注意力的体系结构，用于从每个单一视图中学习节点表示，其网络参数受新颖的正则化术语的约束。为了在不同视图中协作地集成多种类型的关系，探索了一种以视图为中心的注意力方法来聚合各个视图节点表示。我们在几个现实世界的数据集上评估了该算法，并且证明了该方法优于现有的最新基准。