Network embedding has recently garnered attention due to the ubiquity of the networked data in the real-world. A network is useful for representing the relationships among objects, and these network include social network, publication network, and protein-protein interaction network. Most existing network embedding methods assume that only a single type of relation exists between nodes. However, we focus on the fact that two nodes in a network can be connected by multiple types of relations; such a network is called multi-view network or multiplex network. Although several existing work consider the multiplexity of a network, they overlook node attributes, resort to node labels for training, and fail to model the global properties of a graph. In this work, we present an unsupervised network embedding method for attributed multiplex network called DMGI, inspired by Deep Graph Infomax (DGI) that maximizes the mutual information between local patches of a graph, and the global representation of the entire graph. Building on top of DGI, we devise a systematic way to jointly integrate the node embeddings from multiple graphs by introducing 1) the consensus regularization framework that minimizes the disagreements among the relation-type specific node embeddings, and 2) the universal discriminator that discriminates true samples regardless of the relation types. We also show that the attention mechanism infers the importance of each relation type, and thus can be useful for filtering unnecessary relation types as a preprocessing step. We perform comprehensive experiments not only on unsupervised downstream tasks, such as clustering and similarity search, but also a supervised downstream task, i.e., node classification, and demonstrate that DMGI outperforms the state-of-the-art methods, even though DMGI is fully unsupervised. The source code is published here.¹

由于网络数据在现实世界中无处不在，因此网络嵌入最近受到了关注。网络对于表示对象之间的关系很有用，这些网络包括社交网络，发布网络和蛋白质-蛋白质相互作用网络。大多数现有的网络嵌入方法都假定节点之间仅存在一种类型的关系。但是，我们关注一个事实，即网络中的两个节点可以通过多种类型的关系进行连接。这样的网络称为多视图网络或多路复用网络。尽管一些现有的工作考虑了网络的复用性，但它们忽略了节点属性，诉诸于节点标签进行训练，并且无法对图形的全局属性建模。在这项工作中，我们提出了一种用于属性复用网络的无监督网络嵌入方法，称为 DMGI受到Deep Graph Infomax（DGI）的启发，Dmax最大化了图形的局部图块与整个图形的全局表示之间的相互信息。在DGI的基础上，我们设计了一种系统的方法，通过引入1）最小化关系类型特定节点嵌入之间的分歧的共识正则化框架，以及2）区分真实值的通用标识符，来联合集成来自多个图的节点嵌入样本，与关系类型无关。我们还表明，注意力机制可以推断每种关系类型的重要性，因此可以作为预处理步骤用于过滤不必要的关系类型。我们不仅对无监督的下游任务（例如聚类和相似性搜索）进行全面实验，还对无监督的下游任务（即  尽管DMGI 完全不受监督，但DMGI的表现优于最新方法 。源代码在这里发布。^1个