Endorsement Deduction and Ranking in Social Networks

TL;DR

This paper introduces a new method for computing authority scores in social network endorsements by considering relationships among different skills and applying weighted PageRank on enriched endorsement graphs.

Contribution

It proposes an innovative approach that integrates skill relationships into endorsement ranking using a weighted PageRank-based method.

Findings

Method successfully tested on a synthetic network of 1493 nodes.

Enriched endorsement graphs improve authority score accuracy.

Demonstrates the potential for more nuanced skill endorsement rankings.

Abstract

Some social networks, such as LinkedIn and ResearchGate, allow user endorsements for specific skills. In this way, for each skill we get a directed graph where the nodes correspond to users' profiles and the arcs represent endorsement relations. From the number and quality of the endorsements received, an authority score can be assigned to each profile. In this paper we propose an authority score computation method that takes into account the relations existing among different skills. Our method is based on enriching the information contained in the digraph of endorsements corresponding to a specific skill, and then applying a ranking method admitting weighted digraphs, such as PageRank. We describe the method, and test it on a synthetic network of 1493 nodes, fitted with endorsements.