adjacency matrix with missing values. The task is to complete the matrix by identifying the missing values. Matrix factorization based methods commonly use this formulation. History The task of link prediction has attracted attention from several research communities ranging from statistics and network science to machine learning and data mining. In statistics, generative random graph models such as stochastic block models propose an approach to generate links between nodes in a random graph. For social networks, Liben-Nowell and Kleinberg proposed a link prediction models based on different graph proximity measures. Several statistical models have been proposed for link prediction by the machine learning and data mining community. For example, Popescul et al. proposed a structured logistic regression model that can make use of relational features. Local conditional probability models based on attribute and structural features were proposed by O’Madadhain et al. Several models based on directed graphical models for collective link prediction have been proposed by Getoor. Other approached based on random walks. and matrix factorization have also been proposed With the advent of deep learning, several graph embedding based approaches for link prediction have also been proposed. For more information on link prediction refer to the survey by Getoor et al. and Yu et al. Approaches and methods Several link predication approaches have been proposed including unsupervised approaches such as similarity measures computed on the entity attributes, random walk and matrix factorization based approaches, and supervised approaches based on graphical models and deep learning. Link prediction approaches can be divided into two broad categories based on the type of the underlying network: (1) link prediction approaches for homogeneous networks (2) link prediction approaches for heterogeneous networks. Based on the type of information used to predict links, approaches can be categorized as topology-based approaches, content-based approaches, and mixed methods. Topology-based methods Topology-based methods broadly make the assumption that nodes with similar network structure are more likely to form a link. Common neighbors This is a common approach to link prediction that computes the number of common neighbors. Entities with more neighbors in common are more likely to have a link. It is computed as follows: A weakness of this approach is that it does not take into account the relative number of common neighbors. Jaccard measure The Jaccard Measure addresses the problem of Common Neighbors by computing the relative number of neighbors in common: Adamic–Adar measure The Adamic–Adar measure is