  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.
