Long Distance In-Links for Ranking Enhancement | Marco Grassia

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.

Publication
International Symposium on Intelligent and Distributed Computing 2018
Marco Grassia
Marco Grassia
Research Fellow · Network Science and Machine Learning

Research Fellow in Network Science and Machine Learning at University of Catania

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