A Survey of Link Prediction in Complex Networks

Abstract

Networks have become increasingly important to model complex systems composed of interacting elements. Network data mining has a large number of applications in many disciplines including protein-protein interaction networks, social networks, transportation networks, and telecommunication networks. Different empirical studies have shown that it is possible to predict new relationships between elements attending to the topology of the network and the properties of its elements. The problem of predicting new relationships in networks is called link prediction. Link prediction aims to infer the behavior of the network link formation process by predicting missed or future relationships based on currently observed connections. It has become an attractive area of study since it allows us to predict how networks will evolve. In this survey, we will review the general-purpose techniques at the heart of the link prediction problem, which can be complemented by domain-specific heuristic methods in practice.

Connecting Patterns Inspire Link Prediction in Complex Networks

“…Third, we reassign the scores of each link = PNR( ), where is the original similarity score by the first step. Finally, we sort links in the descending order of and links with top-L scores are predicted as potential links [16,18]. The optimal score set opt corresponds to the original similarity scores whose reassigned scores rank in the top-L score list.…”

“…Link prediction is a straightforward approach to retrieve networks by predicting missing links and distinguishing spurious links [15][16][17]. Thus great efforts have been devoted to link prediction in recent years [16,18]. Link prediction is used in different kinds of networks, including unipartite networks and bipartite networks, where unipartite networks consist of nodes with the same type (e.g., social networks and neural networks) and bipartite networks consist of nodes with two types (e.g., user-object purchasing networks and user-movie networks) [19,20].…”

Link prediction for interdisciplinary collaboration via co-authorship network

“…Due to the popularity of link prediction in a wide range of applications, many efforts have been made in developing statistical methods for link prediction problems. Liben-Nowell and Kleinberg (2007), Lü and Zhou (2011) and Martínez et al (2016), among others, are some recent survey papers on this topic. The methods developed can be roughly categorised into model-free and model-based methods.…”

Inter-variable correlation prediction with fuzzy connected-triples

“…Recent studies have extended such classical metrics by adding weights to the existing links within a topological graph in response to the information obtained from explicitly related sources (Lü and Zhou 2010). Also, probabilistic methods have been proposed to handle different forms of link prediction under uncertainty (Martínez et al 2017). However, typical existing approaches (including all discussed above) are set for a specific problem within a local scope, dealing with the information coming from a single data source.…”