Endorsement deduction and ranking in social networks

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.

Synthetic generation of social network data with endorsements

“…We first illustrate our experimental design with a small example, taken from (Pérez-Rosés et al, 2014). The example considers only five skills: 1.…”

“…In many simulation studies involving social networks there is the need to count on a sample network to perform the simulation experiments (e.g. Cointet and Roth, 2007;Fowler and Christakis, 2008;Fowler et al, 2009;Menges et al, 2008;Pan et al, 2012;Pérez-Rosés et al, 2014;Stocker et al, 2002). Due to privacy concerns, most social networks do not disclose sensitive information of its members to outsiders, which may include the set of acquaintances or the endorsements.…”

“…(Pérez-Rosés et al, 2014) for a small community of LinkedIn members, resulting in the matrix M of Eq. 2.…”

Building Information Modelling Skills in Construction Related Disciplines: A Social Network and Job Advertisement-Based Comparative Analysis

“…Other studies have also attempted to explore data such as the endorsements and profiles from social networking websites. However, the focus on endorsements includes proposing new methods to compute artificial data (Pérez-Rosés and Sebé, 2015), and improve data consistency (Pérez-Rosés and Sebé, 2016). Also, several studies focused on exploring social networking profiles to provide recommendations for improving employability (Zide and Shanani-Denning, 2014;Chiang and Suen, 2015).…”