Introducing new Link Prediction Measures and an Evaluation Metric

Abstract

Link prediction plays a critical role in network analysis as it tackles the task of predicting missing or future connections within a given network. A wide array of link prediction measures has been proposed to estimate the likelihood of link existence between nodes, aiming to uncover hidden relationships and anticipate the formation of new connections. This task holds particular significance in various domains, including social networks, biological networks, transportation networks, and recommender systems. Nonetheless, evaluating the effectiveness of these measures poses challenges, encompassing both the measures themselves and the employed evaluation metrics. This article offers a comprehensive overview of the existing link prediction measures, shedding light on their limitations and addressing the associated challenges in evaluating their efficacy. Moreover, we propose enhancements to overcome these challenges, entailing modifications to existing measures and the introduction of novel evaluation metrics. By tackling these issues head-on, our objective is to enhance the accuracy and reliability of link prediction in network analysis.