The entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

现实世界网络的实体通过不同类型的连接（即层）连接。多路复用网络中链路预测的任务是基于层内和层间相关性寻找缺失的连接。我们的观察证实，在广泛的现实世界多重网络中，从社会到生物和技术，一层的连接概率与其他层的相似性之间存在正相关。因此，设计了一种基于相似性的自动通用多路复用链路预测方法——SimBins——根据在多路复用网络中观察到的层间相关性来量化连接不确定性的数量。此外，SimBins 通过结合跨层链接重叠的影响来提高目标层的预测质量。将 SimBins 应用于来自不同领域的各种数据集，我们的研究结果表明，在预测链接时，在大多数情况下，SimBins 的性能优于比较方法（基线和最先进的方法）。此外，还讨论了 SimBins 对基本相似性度量施加较小的计算开销，使其成为一种潜在的快速方法，适用于大规模多路复用网络。