In recent years, unsupervised and self-supervised graph representation learning has gained popularity in the research community. However, most proposed methods are focused on homogeneous networks, whereas real-world graphs often contain multiple node and edge types. Multiplex graphs, a special type of heterogeneous graphs, possess richer information, provide better modeling capabilities and integrate more detailed data from potentially different sources. The diverse edge types in multiplex graphs provide more context and insights into the underlying processes of representation learning. In this paper, we tackle the problem of learning representations for nodes in multiplex networks in an unsupervised or self-supervised manner. To that end, we explore diverse information fusion schemes performed at different levels of the graph processing pipeline. The detailed analysis and experimental evaluation of various scenarios inspired us to propose improvements in how to construct GNN architectures that deal with multiplex graphs.

在本文中，我们探索在无监督或半监督方式下，针对多通道网络中的节点学习表征的问题，并通过在图处理过程的不同层次上进行多样化的信息融合方案的详细分析和实验评估，提出了在处理多通道网络时如何构建GNN架构的改进方法。

多重网络中的表示学习：信息融合的位置和方法