Abstract
Link prediction (LP) permits to infer missing or future connections in a network. The network organization defines how information spreads through the nodes. In turn, the spreading may induce changes in the connections and speed up the network evolution. Although many LP methods have been reported in the literature, as well some methodologies to evaluate them as a classification task or ranking problem, none have systematically investigated the effects on spreading and the structural network evolution. Here, we systematic analyze LP algorithms in a framework concerning: (1) different diffusion process – Epidemics, Information, and Rumor models; (2) which LP method most improve the spreading on the network by the addition of new links; (3) the structural properties of the LP-evolved networks. From extensive numerical simulations with representative existing LP methods on different datasets, we show that spreading improve in evolved scale-free networks with lower shortest-path and structural holes. We also find that properties like triangles, modularity, assortativity, or coreness may not increase the propagation. This work contributes as an overview of LP methods and network evolution and can be used as a practical guide of LP methods selection and evaluation in terms of computational cost, spreading capacity and network structure.
Highlights
The emergence of social media has attracted considerable attention from researchers and companies
We consider as the baseline the random addition of links (RN)
We summarize the effects of adding new edges on the networks according to Link prediction (LP) methods concerning the spreading capacity results, as follow: www.nature.com/scientificreports
Summary
The emergence of social media has attracted considerable attention from researchers and companies. Given the relevance for different domains and areas, research topics such as Link Prediction (LP)[1,2,3] and information diffusion[4,5,6] have received substantial attention in complex and social networks area during the last years[2,3,4] They are topics mostly studied in separated, even that their results are applied in similar domains, like viral marketing, political campaigns, and business process modeling. LP methods estimate the new edges according to some connection strategies, like the distance and shortest paths among nodes, the triangles or triadic closure, the similarity with mutual neighbors, among others[1,2,7,8,9] These structural factors are vital in interpreting networks evolution. The previous studies helped to identify valuable insights into the diffusion processes, they disregard the effects of the dynamic evolution of connections
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