Precision as a measure of predictability of missing links in real networks

García-Pérez, Guillermo; Aliakbarisani, Roya; Ghasemi, Abdorasoul; Serrano, M. Ángeles

doi:10.1103/physreve.101.052318

Cited by 22 publications

(22 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results also resolve the controversy among earlier reports on link prediction using hyperbolic geometry [24,[33][34][35][36][37]. These reports approached link prediction using different measures of link prediction accuracy.…”

Section: Summary Discussion and Conclusionsupporting

confidence: 84%

“…On 043113-12 one hand, the HYPERLINK accuracy appears to increase as 1 − q increases. This effect is consistent with a recent observation in [24] that the upper bound of link predictability in edge-independent graphs increases with 1 − q. On the other hand, as 1 − q increases, the accuracy of RA decreases.…”

Section: A Tests On Rhgs With Inferred Coordinatessupporting

confidence: 92%

“…These results emphasize that the HYPERLINK is definitely not the universally best link prediction method, which simply cannot exist as was recently shown in [24,[39][40][41]. That is, there can exist no one size fits all solution for the link prediction problem.…”

mentioning

confidence: 68%

“…In doing so we pursue several goals. The RHGs provide the upper bound for link prediction accuracy of the HYPERLINK if the same node coordinates are used both for the graph construction and for link prediction [24], so that we want to quantify this upper bound. Second, we want to measure link prediction accuracies of other methods, listed in Appendix B, and compare them to that of the HYPERLINK.…”

Section: Link Prediction With Known Coordinatesmentioning

confidence: 99%

“…Given the combination of these attractive properties, one could naturally expect that the hyperbolic latent space model must be a powerful tool in link prediction. Yet the previous studies on this subject reported mixed results [24,[33][34][35][36][37].…”

mentioning

confidence: 92%

See 4 more Smart Citations

Link prediction with hyperbolic geometry

Kitsak

Voitalov

Krioukov

2020

Phys. Rev. Research

View full text Add to dashboard Cite

Link prediction is a paradigmatic problem in network science with a variety of applications. In latent space network models this problem boils down to ranking pairs of nodes in the order of increasing latent distances between them. The network model with hyperbolic latent spaces has a number of attractive properties suggesting it must be a powerful tool to predict links, but the past work in this direction reported mixed results. Here we perform a systematic investigation of the utility of latent hyperbolic geometry for link prediction in networks. We first show that some measures of link prediction accuracy are extremely sensitive with respect to inaccuracies in the inference of latent hyperbolic coordinates of nodes. This observation leads us to the development of a hyperbolic network embedding method, the HYPERLINK embedder, which we show maximizes the accuracy of such inference, compared to existing hyperbolic embedding methods. Applying this method to synthetic and real networks, we then find that when it comes to predicting obvious missing links hyperbolic link prediction-for short, HYPERLINK-is rarely the best but often competitive, compared to a multitude of other methods. However, HYPERLINK appears to be at its best, maximizing its competitive power, when the task is to predict less obvious missing links that are really hard to predict. These links include missing links in incomplete networks with large fractions of missing links, missing links between nodes that do not have any common neighbors, and missing links between dissimilar nodes at large latent distances. Overall these results suggest that the harder a specific link prediction task the more seriously one should consider using hyperbolic geometry.

show abstract