Term-Specific Eigenvector-Centrality in Multi-Relation Networks

Fuzzy matching and ranking are two information retrieval techniques widely used in web search. Their application to structured data, however, remains an open problem. This article investigates how eigenvector-centrality can be used for approximate matching in multi-relation graphs, that is, graphs where connections of many different types may exist. Based on an extension of the PageRank matrix, eigenvectors representing the distribution of a term after propagating term weights between related data items are computed. The result is an index which takes the document structure into account and can be used with standard document retrieval techniques. As the scheme takes the shape of an index transformation, all necessary calculations are performed during index tim

Recommendation networks in human resource selection

Um dos principais desafios encontrados num processo de recrutamento de recursos humanos prende-se com a dificuldade prática de analisar de forma objetiva todos os candidatos. Neste trabalho mostra-se a aplicabilidade das redes de recomendações profissionais entre pares no âmbito de processos de recrutamento de recursos humanos. Para o efeito utilizamos a rede social LinkedIn e em particular as recomendações efetuadas por um conjunto de profissionais. Geramos uma rede de recomendações por especialidade, em que os vértices são os profissionais analisados e os arcos são as relações de recomendação. Sobre a rede de recomendações aplica-se o algoritmo PageRank para ordenar cada profissional por especialidade. A análise para várias especialidades é realizada através da avaliação multicritério, onde é aplicado o método TOPSIS.One of the main challenges in a human resources recruitment process is the difficulty of analyzing objectively all candidates. This paper shows the application of networks of professional recommendations among peers in human resources recruitment processes. For this purpose we use the LinkedIn social network and in particular the recommendations made by a subset of professionals. We generate a network of recommendations by specialty, where the vertices are the professionals and the arcs are the recommendation relationships. Based on the network of recommendations we apply the algorithm PageRank to order of each professional by specialty. The analysis for several specialties is performed using multi-criteria evaluation, where the TOPSIS method is applied.info:eu-repo/semantics/publishedVersio

Synthetic Generation of Social Network Data With Endorsements

In many simulation studies involving networks there is the need to rely on a sample network to perform the simulation experiments. In many cases, real network data is not available due to privacy concerns. In that case we can recourse to synthetic data sets with similar properties to the real data. In this paper we discuss the problem of generating synthetic data sets for a certain kind of online social network, for simulation purposes. Some popular online social networks, such as LinkedIn and ResearchGate, allow user endorsements for specific skills. For each particular skill, the endorsements give rise to a directed subgraph of the corresponding network, where the nodes correspond to network members or users, and the arcs represent endorsement relations. Modelling these endorsement digraphs can be done by formulating an optimization problem, which is amenable to different heuristics. Our construction method consists of two stages: The first one simulates the growth of the network, and the second one solves the aforementioned optimization problem to construct the endorsements.Comment: 5 figures, 2 algorithms, Journal of Simulation 201

A logic of negative trust

We present a logic to model the behaviour of an agent trusting or not trusting messages sent by another agent. The logic formalises trust as a consistency checking function with respect to currently available information. Negative trust is modelled in two forms: distrust, as the rejection of incoming inconsistent information; mistrust, as revision of previously held information becoming undesirable in view of new incoming inconsistent information, which the agent wishes to accept. We provide a natural deduction calculus, a relational semantics and prove soundness and completeness results. We overview a number of applications which have been investigated for the proof-theoretical formulation of the logic

The Index of Philanthropic Freedom: Selected Country Reports

In 2015, the Hudson Institute's Center for Global Prosperity (CGP) published the first edition of the Index of Philanthropic Freedom (IPF). The IPF, which scores and ranks 64 countries on their enabling environments for philanthropy, is based on the detailed responses of country experts to a seven question survey produced by CGP in consultation with survey design experts. This document provides 21 of the 64 country expert reports received by the Center for Global Prosperity. It was prepared for Worldwide Initiatives for Grantmaker SupportInternational Meeting on the Enabling Environment for Philanthropy in Lisbon, Portugal, March 10th - 11th 2016

The Perception of LGBTQ Influencers on Social Media

Social media brand influencers have become one of the biggest marketing and public relations trends of 2017, especially those who promote lifestyle brands (Glucksman, 2017). While many social media users are capitalizing on the “brand influencer” trend, people who identify as the LGBTQ, still struggle to connect with the heterosexual audience as brand influencers source. YouTube and Instagram are visual mediums that allow brand influencers to craft their expertise verbally and visually to communicate their expertise and enhance credibility. Using the source credibility theory, which posits that persuasiveness of the message is based on the perceived credibility of the source (Hovland et al, 1951), this study investigates how the LGBTQ and heterosexual influencers were perceived by the followers on the YouTube and Instagram platform. Using a Netnography approach, (n=4,646) comments were analyzed from YouTube and (n=16,683) from Instagram to identify positive, negative, and neutral sentiment. Findings demonstrate the LGBTQ community have a higher level of engagement, but at the same time, a high interest from followers on the sponsored posts. The commenters responding to the heterosexual influencers focus more on the influencer appearance rather than the content presented by them. Comments on Instagram are more explicit compared to YouTube

O algoritmo PageRank aplicado a redes de recomendações para seleção de recursos humanos

Um dos principais desafios encontrados num processo de recrutamento de recursos humanos prende-se com a dificuldade prática de analisar de forma objetiva todos os candidatos. A grande quantidade de curricula vitae normalmente envolvida num processo deste tipo dificulta, quando não impede mesmo, uma leitura completa e, portanto, uma análise detalhada. Neste trabalho procura mostrar-se a aplicabilidade do algoritmo PageRank a redes de recomendações profissionais entre pares no âmbito de processos de recrutamento de recursos humanos. Assim, através de técnicas de data-mining, procedeu-se à extracção de dados de teste do conteúdo HTML de páginas de alguns profissionais registados no site LinkedIn, concretamente as recomendações efectuadas por cada um para um conjunto de outros profissionais por especialidade. Com os dados extraídos contruiram-se redes de recomendações por especialidade, em que os vértices são os profissionais analisados e os arcos são as relações de recomendação, redes sobre as quais se aplicou o algoritmo PageRank para classificação ordenada de cada profissional por especialidade. A extensão para várias especialidades é realizada através de um algoritmo de avaliação multicritério que, aplicado aos valores de PageRank, possibilita a classificação de um profissional tendo em conta mais do que uma especialidade.One of the main challenges encountered in a human resource recruitment process is the practical difficulty of analysing all candidates in an objective manner. The large amount of curricula vitae normally involved prevents a thorough reading and therefore a detailed analysis. This work aims to show the applicability of link-analysis techniques to networks of professional recommendations among peers in the scope of recruitment processes of human resources. So, using data-minign techniques, we proceed with the extraction of test data from the HTML content of some LinkedIn pages, moreover the recommendations made between members. This extraction allowed the construction of a network of recommendations by skill, in which the nodes are professionals and the arcs are the recommendarions. Over these networks we applied the PageRank Algorithm in order to serialize each professional by skill. The expansion to more than one skill is made through the use of an algorithm of multi-criteria evaluation, applied to the various PageRank values of each skill

Developing and validating measurement items for a multi-dimensional social network site usage construct

SNS platforms are providing simultaneously for hedonic and utilitarian type uses. However, extant research continues to model the SNS usage construct as a simplistic unidimensional construct that fails to adequately reflect the multi-dimensional nature of SNS usage in workplace contexts. This paper contributes by presenting results of a multi-phase process used to develop and validate measures of the deep structure SNS usage construct from both hedonic and utilitarian perspectives. Psychometric tests were conducted using 124 usable responses, and the results show that deep structure usage is best modelled as a reflective second order construct with three first order dimensions reflecting hedonic use, utilitarian use, and cognitive absorption. The multi-dimensional deep usage SNS usage construct will be of interest to researchers examining SNS usage in the workplace and its implications for workplace outcomes. Implications for practice, including SNS design and usage policy, are also described