Abstract
The entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.
Highlights
The entities of real-world networks are connected via different types of connections
Through the incorporation of various similarity indices (RA, Common Neighbors (CN), Average Commute Time (ACT) and Local Path Index (LPI)) as the base proximity measures, we demonstrate that the proposed method -SimBins- can be used to predict multiplex links without degrading the time complexity significantly
To be able to evaluate the proposed method, ET i.e. the edges in target layer is divided into EanOtnoeotTtnecrdtslatthye )eie,ndtpdhioanieeunitgritesnpEsroeufmtTortftariiEEnonmt Tt.TfirenaaTstittghnoiaoet(nphn9pdue0prt%aporiopetrorvaoiffninsodEedreadmTodf m)aeabalwnygnscoutdwerhbiooaesthrfettdtmrethsasZ;tient(ToeslmisinentntleogykEtf-hsatTUeeeosxtstdiu−i(s.sb1tTeuEs0ones%TtecbdewooefilfhminnkEeoroetThelrn i)ee|h-ZsoopsotTpbroeseetsdthedc|raiis=fcvtcitceoEi,d2ortTlr|eniaElsniinnttTkefakos∪stlsrki|kfEiaaoentTnlresistduhwtrboa=ehsiovniedecEtinhnsTotgcaufaolasUlnreleodltys−f,aEatErhtTrEertTeeaetTirssmcnatciaion∩sla,rcrceuiEeondltTmaedcfstlitouepsrd=cdaoritfnehno∅dger- . sake of complexity which will be discussed in detail later
Summary
The entities of real-world networks are connected via different types of connections (i.e., layers). Common Neighbors (CN)[1], Preferential Attachment (PA)[8], Adamic-Adar (AA)[9] and Resource Allocation (RA)[10] are popular indices focusing mostly on nodes’ structural features, each with unique characteristics Even though these indexes are simple, they are popular because of their low computational cost and reasonable prediction performance. Some researchers have tackled the link prediction problem using the ideas of information theory These works are based on the fact that similarity of node pairs can be written in term of the uncertainty of their connectivity. Path Entropy (PE)[18] similarity index takes quantity and length of paths as well as theirentropy into account This results in a better assessment of connection likelihood for node pairs. The results are promising when compared to both weighted and unweighted methods
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
