Multiplex networks are a representation of real-world complex systems as a set 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 the link prediction task is about locating the missing links across layers. Here, the main challenge is about collecting relevant evidence from different layers to assist the link prediction task.

It is known that co-membership in communities increases the likelihood of connectivity between nodes. We discuss that co-membership in the communities of the similar layers augments the chance of connectivity. The layers are considered similar if they show significant inter-layer community overlap. Moreover, we found that although the presence of link is correlated in layers but the extent of this correlation is not the same across different communities. Our proposed, ML-BNMTF, as a link prediction method in multiplex networks, is devised based on these findings. ML-BNMTF outperforms baseline methods specifically when the global link overlap is low.

复用网络是现实世界中复杂系统的表示，它是一组通过不同类型的连接（即层）连接的实体（即节点）。这些网络中观察到的连接可能不完整，并且链接预测任务是关于跨层定位丢失的链接。在这里，主要的挑战是要从不同的层收集相关证据以协助链接预测任务。

众所周知，社区中的共同成员身份会增加节点之间进行连接的可能性。我们讨论了相似层的社区中的共同成员身份会增加连接的机会。如果这些层显示出显着的层间社区重叠，则认为它们是相似的。此外，我们发现尽管链接的存在在各个层中是相关的，但是在不同的社区中，这种相关的程度并不相同。基于这些发现，设计了我们提出的ML-BNMTF作为多路复用网络中的链路预测方法。ML-BNMTF优于基线方法，特别是在全局链接重叠低的情况下。