The maximum capability of a topological feature in link prediction

Abstract

Link prediction aims to predict links of a network that are not directly visible, with profound applications in biological and social systems. Despite intensive utilization of the topological feature in this task, it is unclear to what extent a particular feature can be leveraged to infer missing links.Here, we show that the maximum capability of a topological feature follows a simple mathematical expression, which is independent of how an index gauges the feature. Hence, a family of indexes associated with one topological feature shares the same performance limit. A feature's capability is lifted in the supervised prediction, which in general gives rise to better results compared with unsupervised prediction. The universality of the pattern uncovered is empirically verified by 550 structurally diverse networks, which can be applied to feature selection and the analysis of network characteristics associated with a topological feature in link prediction.

Reconstructing supply networks

“…propelling the development of computer-vision models [71]. They are the reference evaluation metrics for link-prediction problems [15], and the theoretical analysis of their properties is still an active field [72]. Yet, the question remains as to whether they are sufficient to evaluate the reconstruction of a network, and what additional metrics should be adopted to supplement them.…”