Uncovering missing links with cold ends

TL;DR

This paper investigates the challenge of predicting missing links involving low-degree nodes in networks, revealing that the Leicht-Holme-Newman index outperforms others under realistic sampling conditions and proposing an improved index for better accuracy.

Contribution

The study uncovers the bias in standard link prediction evaluation and introduces a parameter-dependent index that enhances prediction of low-degree node links.

Findings

Leicht-Holme-Newman index performs best for low-degree node links.

Proposed index significantly improves prediction accuracy.

Validation on real sampling methods confirms effectiveness.

Abstract

To evaluate the performance of prediction of missing links, the known data are randomly divided into two parts, the training set and the probe set. We argue that this straightforward and standard method may lead to terrible bias, since in real biological and information networks, missing links are more likely to be links connecting low-degree nodes. We therefore study how to uncover missing links with low-degree nodes, namely links in the probe set are of lower degree products than a random sampling. Experimental analysis on ten local similarity indices and four disparate real networks reveals a surprising result that the Leicht-Holme-Newman index [E. A. Leicht, P. Holme, and M. E. J. Newman, Phys. Rev. E 73, 026120 (2006)] performs the best, although it was known to be one of the worst indices if the probe set is a random sampling of all links. We further propose an parameter-dependent index, which considerably improves the prediction accuracy. Finally, we show the relevance of the proposed index on three real sampling methods.