Predicting missing links and their weights via reliable-route-based method

TL;DR

This paper introduces a reliable-route-based method to extend unweighted link prediction algorithms to weighted networks, improving the prediction of both missing links and their weights.

Contribution

It proposes a novel reliable-route-based extension for local similarity indices to predict links and weights in weighted networks, addressing a gap in existing methods.

Findings

Weighted resource allocation index excels in link prediction.

Reliable-route-based weighted resource allocation improves weight prediction.

Higher clustering coefficient correlates with better prediction accuracy.

Abstract

Link prediction aims to uncover missing links or predict the emergence of future relationships according to the current networks structure. Plenty of algorithms have been developed for link prediction in unweighted networks, with only a very few of them having been extended to weighted networks. Thus far, how to predict weights of links is important but rarely studied. In this Letter, we present a reliable-route-based method to extend unweighted local similarity indices to weighted indices and propose a method to predict both the link existence and link weights accordingly. Experiments on different real networks suggest that the weighted resource allocation index has the best performance to predict the existence of links, while the reliable-route-based weighted resource allocation index performs noticeably better on weight prediction. Further analysis shows a strong correlation for both link prediction and weight prediction: the larger the clustering coefficient, the higher the prediction accuracy.