text: Networks extracted from social media platforms frequently include multiple types of links that dynamically change over time; these links can be used to represent dyadic interactions such as economic transactions, communications, and shared activities. Organizing this data into a dynamic multiplex network, where each layer is composed of a single edge type linking the same underlying vertices, can reveal interesting cross-layer interaction patterns. In coevolving networks, links in one layer result in an increased probability of other types of links forming between the same node pair. Hence we believe that a holistic approach in which all the layers are simultaneously considered can outperform a factored approach in which link prediction is performed separately in each layer. This paper introduces a comprehensive framework, MLP (Multilayer Link Prediction), in which link existence likelihoods for the target layer are learned from the other network layers. These likelihoods are used to reweight the output of a single layer link prediction method that uses rank aggregation to combine a set of topological metrics. Our experiments show that our reweighting procedure outperforms other methods for fusing information across network layers.
  @cite_16: Online social networks have gained great success in recent years. Some online social networks only involving users and social links among users can be represented as homogeneous networks. Meanwhile, some other social networks containing abundant information, which include multiple kinds of nodes and complex relationships, can be denoted as heterogeneous networks. Predicting the missing links or links that will be formed in the future based on a snapshot of social networks is formally defined as the link prediction problem. Link prediction problems have extensive applications in real-world social networks and many concrete social services can be cast as link prediction tasks, e.g., friend and location recommendations can all be solved as the problem of predicting social links among users and the location links between users and locations. Link prediction problems have been an important research topic for many years and a large number of different methods have been proposed so far. This article summaries the existing link prediction methods for both homogeneous and heterogeneous networks, which include various unsupervised link predicators, random walk based link prediction methods, methods based on matrix factorization techniques, supervised link prediction methods and meta paths based link prediction methods. Meanwhile, as proposed in recent works, people are usually involved in multiple social networks simultaneously nowadays and networks sharing common users are formally defined as the aligned networks. In this article, we will also introduce the latest progress of link prediction problems across multiple aligned heterogeneous networks. The link prediction problems across aligned networks can include anchor link prediction problem and link transfer across aligned heterogeneous networks. We will introduce the newly proposed methods to solve these problems in details and, finally, we will conclude this survey with a discussion about the future link prediction research works.
  @cite_1: Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link-prediction problem, and we develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a network. Experiments on large coauthorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures. © 2007 Wiley Periodicals, Inc.
  @cite_25: Link prediction is an important task for analying social networks which also has applications in other domains like, information retrieval, bioinformatics and e-commerce. There exist a variety of techniques for link prediction, ranging from feature-based classification and kernel-based method to matrix factorization and probabilistic graphical models. These methods differ from each other with respect to model complexity, prediction performance, scalability, and generalization ability. In this article, we survey some representative link prediction methods by categorizing them by the type of the models. We largely consider three types of models: first, the traditional (non-Bayesian) models which extract a set of features to train a binary classification model. Second, the probabilistic approaches which model the joint-probability among the entities in a network by Bayesian graphical models. And, finally the linear algebraic approach which computes the similarity between the nodes in a network by rank-reduced similarity matrices. We discuss various existing link prediction models that fall in these broad categories and analyze their strength and weakness. We conclude the survey with a discussion on recent developments and future research direction.
  @cite_3: Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labeled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.
summary: A variety of computational approaches have been employed for predicting links in single layer networks, including supervised classifiers, statistical relational learning, matrix factorization, metric learning, and probabilistic graphical models (see surveys by @cite_3 @cite_25 @cite_16 for a more comprehensive description). Regardless of the computational framework, topological network measures are commonly used as features to describe node pairs and can be combined in a supervised or unsupervised fashion to do link prediction. @cite_1 . In this paper, we aggregate several of these metrics (listed in the next section), but our framework can be easily generalized to include other types of features.