Multiplex networks are complex graph structures in which a set of entities are connected to each other via multiple types of relations, each relation representing a distinct layer. Such graphs are used to investigate many complex biological, social, and technological systems. In this work, we present a novel semi-supervised approach for structure-aware representation learning on multiplex networks. Our approach relies on maximizing the mutual information between local node-wise patch representations and label correlated structure-aware global graph representations to model the nodes and cluster structures jointly. Specifically, it leverages a novel cluster-aware, node-contextualized global graph summary generation strategy for effective joint-modeling of node and cluster representations across the layers of a multiplex network. Empirically, we demonstrate that the proposed architecture outperforms state-of-the-art methods in a range of tasks: classification, clustering, visualization, and similarity search on seven real-world multiplex networks for various experiment settings.

本研究提出了一种新的半监督结构感知表征学习方法，通过最大化局部节点表示和标签相关的全局图表示之间的互信息来联合建模节点和群集结构，并采用聚类感知、节点上下文全局图概括生成策略来有效地联合建模多层复合网络中的节点和群集表示，实验结果表明，提出的方法在分类、聚类、可视化和相似性搜索等任务中优于现有方法。

基于半监督的深度学习用于多重网络