Long Distance In-Links for Ranking Enhancement

Abstract

Ranking is a widely used technique to classify nodes in networks according to their relevance. Increasing one’s rank is a desiderable feature in almost any context; several approaches have been proposed to achieve this goal by exploiting in-links and/or out-links with other existing nodes. In this paper, we focus on the impact of in-links in rank improvement (with PageRank metric) and their distance from starting link. Results for different networks both in type and size show that the best improvement comes from long distance nodes rather than neighbours, somehow subverting the commonly adopted social-based approach.

Marco Grassia

Assistant Professor · Network Science and Machine Learning

Assistant Professor. Researching Network Science and Geometric Deep Learning. University of Catania, Italy