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The entities of real-world networks are connected via different types ofconnections (i.e. layers). The task of link prediction in multiplex networks isabout finding missing connections based on both intra-layer and inter-layercorrelations. Our observations confirm that that in a wide range of real-worldmultiplex networks, from social to biological and technological, a positivecorrelation exists between connection probability in one layer and similarityin other layers. Accordingly, a similarity-based automatic general-purposemultiplex link prediction method -- SimBins -- is devised that quantifies theamount of connection uncertainty based on observed inter-layer correlations ina multiplex network. Moreover, SimBins enhances the prediction quality in thetarget layer by incorporating the effect of link overlap across layers. Appliedto various datasets from different domains, SimBins proves to be robust andsuperior than compared methods in majority of experimented cases in terms ofaccuracy of link prediction. Furthermore, it is discussed that SimBins imposesminor computational overhead to the base similarity measures making it apotentially fast method, suitable for large-scale multiplex networks.

SimBins: An information-theoretic approach to link prediction in real multiplex networks