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Online social network (OSN) applications provide different experiences; forexample, posting a short text on Twitter and sharing photographs on Instagram.Multiple OSNs constitute a multiplex network. For privacy protection and usagepurposes, accounts belonging to the same user in different OSNs may havedifferent usernames, photographs, and introductions. Interlayer link predictionin multiplex network aims at identifying whether the accounts in different OSNsbelong to the same person, which can aid in tasks including cybercriminalbehavior modeling and customer interest analysis. Many real-world OSNs exhibita scale-free degree distribution; thus, neighbors with different degrees mayexert different influences on the node matching degrees across different OSNs.We developed an iterative degree penalty (IDP) algorithm for interlayer linkprediction in the multiplex network. First, we proposed a degree penaltyprinciple that assigns a greater weight to a common matched neighbor with fewerconnections. Second, we applied node adjacency matrix multiplication forefficiently obtaining the matching degree of all unmatched node pairs.Thereafter, we used the approved maximum value method to obtain the interlayerlink prediction results from the matching degree matrix. Finally, theprediction results were inserted into the priori interlayer node pair set andthe above processes were performed iteratively until all unmatched nodes in onelayer were matched or all matching degrees of the unmatched node pairs wereequal to 0. Experiments demonstrated that our advanced IDP algorithmsignificantly outperforms current network structure-based methods when themultiplex network average degree and node overlapping rate are low.

Interlayer link prediction in multiplex social networks: an iterative degree penalty algorithm