A Survey on Link Prediction Problem in Social Networks

Abstract

Link prediction is a crucial task in analysis of online social networks. It can be used for friend recommendations in social networks. It also has applications in other areas like bioinformatics, information retrieval and e-commerce. Currently, variety of techniques is available to deal with link prediction problem, ranging from local feature based approaches to global feature based approaches. Some recent techniques combine the advantages of these two approaches and comes under hybrid category. all these methods differ from each other with respect to prediction performance in terms of accuracy, efficiency, and generalization ability. In this paper, we survey some representative link prediction techniques under each category. We largely consider three types of approaches. First category is of local feature based approaches which do not exploit the whole network structure. Second category is of global feature based approaches which use the overall path structure of a network. And, finally the hybrid approaches which combine the advantages of above two approaches (local and global) in computation of similarity scores between each pair of nodes in a network. We discuss some recent existing approaches corresponding to these broad categories and analyze their strength and weaknesses. We conclude the paper with discussion on recent developments and future research direction.

A review of similarity measures and link prediction models in social networks

“…In citation networks, scholars can find the core papers that will be potentially useful in future [19]. In security related applications, it is used to identify criminals or terrorists [20]. It can be applied to potential collaborators for providing related items in online shopping, recommending patent partners, predicting the mobile contacts in large scale communication network, predicting hyperlinks in heterogeneous social network, understanding the evolution of networks better, in health care and gene expression networks, viral marketing, social mobilization etc [18], [21].…”

“…5. Both these methods are used to find the similarity between two nodes in the network structure by estimating the proximity between two nodes [20], [22], [23]. The similarity or connection between the nodes can be established by studying their common features [24].…”

Link Prediction in Complex Networks using Embedding Techniques and Similarity Measures

“…Preferential Attachment: A preferential attachment process is one of those processes in which some asset is distributed among the network users in a biased way so that the rich user nodes receive more than others. [14] Friend TNS score: The basic function of the friend TNS algorithm is to evaluate the similarities from a specific user node to all other user nodes present in a network, using our fundamental as well as derived node similarity measures [11].…”