The maximum capability of a topological feature in link prediction.

ABSTRACT: Networks offer a powerful approach to modeling complex systems by representing the underlying set of pairwise interactions. Link prediction is the task that predicts links of a network that are not directly visible, with profound applications in biological, social, and other complex systems. Despite intensive utilization of the topological feature in this task, it is unclear to what extent a feature can be leveraged to infer missing links. Here, we aim to unveil the capability of a topological feature in link prediction by identifying its prediction performance upper bound. We introduce a theoretical framework that is compatible with different indexes to gauge the feature, different prediction approaches to utilize the feature, and different metrics to quantify the prediction performance.

SUBMITTER: Ran Y 

PROVIDER: S-EPMC10962729 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature