  Some networked systems can be better modelled by multilayer structure where
the individual nodes develop relationships in multiple layers. Multilayer
networks with similar nodes across layers are also known as multiplex networks.
This manuscript proposes a novel framework for predicting forthcoming or
missing links in multiplex networks. The link prediction problem in multiplex
networks is how to predict links in one of the layers, taking into account the
structural information of other layers. The proposed link prediction framework
is based on interlayer similarity and proximity-based features extracted from
the layer for which the link prediction is considered. To this end, commonly
used proximity-based features such as Adamic-Adar and Jaccard Coefficient are
considered. These features that have been originally proposed to predict
missing links in monolayer networks, do not require learning, and thus are
simple to compute. The proposed method introduces a systematic approach to take
into account interlayer similarity for the link prediction purpose.
Experimental results on both synthetic and real multiplex networks reveal the
effectiveness of the proposed method and show its superior performance than
state-of-the-art algorithms proposed for the link prediction problem in
multiplex networks.
