Why Link Prediction?

link prediction aims at estimating the likelihood of the existence of a link between two nodes, based on observed links and the attributes of nodes. Link prediction helps in analyzing networks with missing data , for example, many biological networks, such as food webs, protein–protein interaction networks and metabolic networks, whether a link between two nodes exists must be demonstrated by field and/or laboratory experiments, which are usually very costly.Our knowledge of these networks is very limited, for example, 80% of the molecular interactions in cells of Yeast [1] and 99.7% of human [2] are still unknown. Instead of blindly checking all possible interactions, to predict based on known interactions and focus on those links most likely to exist can sharply reduce the experimental costs if the predictions are accurate enough.In addition, link prediction algorithms can be used to predict the links that may appear in the future of evolving networks, such as friendship recommendation in online social networks and product recommendation in e-commerce web sites [3]. Further applications of link prediction include the identification of spurious links, estimation of the competing mechanisms in evolving networks, classification of nodes, and so on.
 
Thanks to the fundamental theoretical importance in network sciences and the wide applications, the study of link prediction has attracted much attention recently. As a supportive evidence, a recent survey has accumulated 515 citations for only about 4 years till Aug. 2015, and a number of papers on this topic got published in prestigious journals [5-9].
 
Link prediction will be still a very hot topic in the near future.The major directions in the next five years include (but not being limited) in the four following directions. (i) Some fundamental theoretical issues in link prediction, such as the link predictability of networks. (ii) Link prediction for different kinds of networks, such as directed networks and weighted networks, as well as the prediction of direction and weights, in addition to the existence of a link. (iii) Link prediction algorithms with some novel kind sof data, such as link prediction in social networks with human mobility data.(iv) The connection of link prediction algorithms to some other related topic,such as the understanding of networks evolution and the detection of community structure.
 
[1] H. Yu,  et al. ,Science 322 (2008) 104.
[2] L. A. N. Amaral, PNAS 105 (2008) 6795.
[3] L. Lü,  et al. ,Physics Reports 519 (2012) 1.
[4] L. Lü,  et al. ,Physica A 390 (2011) 1150.
[5] A. Clauset,  et al. ,Nature 453 (2008) 98.
[6] R. Guimera,  et al. ,PNAS 106 (2009) 22073.
[7] B. Barzel,  et al. ,Nature Biotechnology 31 (2013) 720.
[8] P. Bastiaens,  et al. ,Nature Biotechnology 33 (2015) 336.

[9] L. Lü, et al., PNAS 112 (2015) 2325.