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Multiplex networks are a representation of real-world complex systems as aset of entities (i.e. nodes) connected via different types of connections (i.e.layers). The observed connections in these networks may not be complete and thelink prediction task is about locating the missing links across layers. Here,the main challenge is about collecting relevant evidence from different layersto assist the link prediction task. It is known that co-membership incommunities increases the likelihood of connectivity between nodes. We discussthat co-membership in the communities of the similar layers augments the chanceof connectivity. The layers are considered similar if they show significantinter-layer community overlap. Moreover, we found that although the presence oflink is correlated in layers but the extent of this correlation is not the sameacross different communities. Our proposed, ML-BNMTF, as a link predictionmethod in multiplex networks, is devised based on these findings. ML-BNMTFoutperforms baseline methods specifically when the global link overlap is low.

Overlapping Communities and the Prediction of Missing Links in Multiplex Networks