Performance of Local Information Based Link Prediction: A Sampling Perspective

TL;DR

This paper reevaluates local information-based link prediction methods considering different sampling techniques, revealing that their performance varies significantly and is often overestimated under biased sampling conditions.

Contribution

It introduces a sampling-aware evaluation framework for link prediction, highlighting the impact of sampling bias on the performance of existing methods.

Findings

Performance varies across sampling methods

Biased sampling leads to overestimated prediction accuracy

Most methods perform poorly under biased sampling

Abstract

Link prediction is pervasively employed to uncover the missing links in the snapshots of real-world networks, which are usually obtained from kinds of sampling methods. Contrarily, in the previous literature, in order to evaluate the performance of the prediction, the known edges in the sampled snapshot are divided into the training set and the probe set randomly, without considering the diverse sampling approaches beyond. However, different sampling methods might lead to different missing links, especially for the biased ones. For this reason, random partition based evaluation of performance is no longer convincing if we take the sampling method into account. Hence, in this paper, aim at filling this void, we try to reevaluate the performance of local information based link predictions through sampling methods governed division of the training set and the probe set. It is interesting that we find for different sampling methods, each prediction approach performs unevenly. Moreover, most of these predictions perform weakly when the sampling method is biased, which indicates that the performance of these methods is overestimated in the prior works.