  Inferencing with network data necessitates the mapping of its nodes into a
vector space, where the relationships are preserved. However, with
multi-layered networks, where multiple types of relationships exist for the
same set of nodes, it is crucial to exploit the information shared between
layers, in addition to the distinct aspects of each layer. In this paper, we
propose a novel approach that first obtains node embeddings in all layers
jointly via DeepWalk on a \textit{supra} graph, which allows interactions
between layers, and then fine-tunes the embeddings to encourage cohesive
structure in the latent space. With empirical studies in node classification,
link prediction and multi-layered community detection, we show that the
proposed approach outperforms existing single- and multi-layered network
embedding algorithms on several benchmarks. In addition to effectively scaling
to a large number of layers (tested up to $37$), our approach consistently
produces highly modular community structure, even when compared to methods that
directly optimize for the modularity function.
