Abstract
Online s have attracted great attention recently, because they make it easy to build social connections for people all over the world. However, the observed structure of an online is always the aggregation of multiple social relationships. Thus, it is of great importance for real-world networks to reconstruct the full network structure using limited observations. The multiplex is introduced to describe multiple social ties, where different layers correspond to different attributes (e.g., age and gender of users in a ). In this letter, we aim to improve the model precision using maximum likelihood estimation, where the precision is defined by the cross entropy of parameters between the data and model. Within this framework, the layers and partitions of nodes in a are determined by natural node annotations, and the aggregate of the is available. Because the original has a high degree of freedom, we add an independent functional layer to cover it, and theoretically provide the optimal block number of the added layer. Empirical results verify the effectiveness of the proposed method using four measures, i.e., error of link probability, cross entropy, area under the receiver operating characteristic curve, and Bayes factor.
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