Attributed multiplex networks are powerful representations of complex systems where nodes represent entities, their attributes represent the properties, and each type of interaction is modeled as a relationship (layer) in a network. To analyze these networks, it is crucial to find a meaningful representation of nodes, node attributes, and class labels into a joint low-dimensional space. To this end, we propose a Contrastive Joint Embedding approach for Multiple Networks, CJEMN, that employs negative sampling and pseudo-labeling to obtain a meaningful embedding of all information within an attributed multiplex network. To the best of our knowledge, this is the first approach that utilizes negative sampling and pseudo-labeling to jointly embed nodes, node attributes, and class labels of attributed multiplex networks in a low-dimensional space. In addition to using spectral embedding and homogeneity analysis, our method incorporates negative pairs as a new layer to enhance the representation of similarities and dissimilarities among nodes, attributes, and class labels. We run experiments on five real-world datasets to evaluate the performance of CJEMN. Our approach outperforms state-of-the-art methods for downstream tasks, such as node classification and clustering.

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