Supervised-learning link prediction in single layer and multiplex networks

Abstract

The emergence of complex real-world networks has put forth a plethora of information about different domains. Link-prediction is one of the emerging research problems that utilizes the information from the networks to find future relationships between the nodes. The structure of real-world networks varies from having homogeneous relationships to having multiple associations. The homogeneous relationships are modeled by single-layer networks, while the multiplex networks represent the multiple associations. This study proposes a solution for finding future links in single-layer and multiplex networks by using supervised machine learning techniques. This study considers a set of topological features of the network for training the machine learning classifiers. The training and testing data set construction framework devised in this work helps in evaluating the proposed method on different networks. This study also contributes towards identifying four community-based features for the proposed mechanism.