Graph embedding is an important approach for graph analysis tasks such as node classification and link prediction. The goal of graph embedding is to find a low dimensional representation of graph nodes that preserves the graph information. Recent methods like Graph Convolutional Network (GCN) try to consider node attributes (if available) besides node relations and learn node embeddings for unsupervised and semi-supervised tasks on graphs. On the other hand, multi-layer graph analysis has been received attention recently. However, the existing methods for multi-layer graph embedding cannot incorporate all available information (like node attributes). Moreover, most of them consider either type of nodes or type of edges, and they do not treat within and between layer edges differently. In this paper, we propose a method called MGCN that utilizes the GCN for multi-layer graphs. MGCN embeds nodes of multi-layer graphs using both within and between layers relations and nodes attributes. We evaluate our method on the semi-supervised node classification task. Experimental results demonstrate the superiority of the proposed method to other multi-layer and single-layer competitors and also show the positive effect of using cross-layer edges.
翻译:图形嵌入是像节点分类和链接预测等图形分析任务的一个重要方法。 图形嵌入的目标是找到能够保存图形信息的图形节点的低维代表度。 近期的一些方法, 比如“ 图表进化网络”, 试图考虑节点关系之外的节点属性( 如果存在的话), 并在图形中学习无监管和半监管任务中的节点嵌入。 另一方面, 多层图形分析最近受到关注。 但是, 多层图形嵌入的现有方法无法包含所有可用信息( 类似节点属性 ) 。 此外, 大部分这些方法都考虑节点类型或边缘类型, 它们不会在层边缘内部和之间作不同处理 。 在本文中, 我们提出了一个名为“ MGCN” 的方法, 将 GCN 用于多层图形。 MGCN 在层关系和节点属性内部和之间嵌入多层图形节点的节点。 我们评估了我们关于半超级节点分类任务的方法 。 实验结果显示拟议方法的优越性, 并用其他多层和单层竞争者的正面效果展示。