Interlayer Link Prediction by Analyzing Matching Degree in Multiplex Networks

Some networks are well-modelled as a multilayer structure in the real world, with interactions between nodes in numerous layers. For example, users may, have accounts on numerous online social networks (OSNs) such as Twitter, Instagram, and Facebook, and each social network can be thought of as a layer in a multiplex network. The goal of predicting links between layers of a multiplex network is to detect if accounts across various OSNs are associated with a same user or not. This can be useful in a variety of situations, such as evaluating client interests or predicting cybercriminal behavior. In this research, we present a unique Interlayer link prediction approach that incorporates information from the degree penalty mechanism. The technique takes advantage of the network's power-law degree distribution. As a result, in different OSNs, neighbors with varying degrees of proximity may have varied effects on the degree of node matching. It can match a user's accounts across many layers well. The suggested strategy outperforms similar methods by at least 10% in terms of prediction accuracy, according to experimental results on both synthetic and real-world networks.