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Type:	Tese
Title:	Random walks on the reputation graph
Authors:	Sabir Ribas
First Advisor:	Berthier Ribeiro-Neto
First Co-advisor:	Nivio Ziviani
First Referee:	Altigran Soares da Silva
Second Referee:	Edmundo Albuquerque Souza e Silva
Third Referee:	Rodrygo Luis Teodoro Santos
Abstract:	The identification of reputable entities is an important task in business, education, and in many other fields. In general, the reputation of an entity reflects its public perception, which touches upon a variety of aspects that may impact the identity of the entity, such as its prowess, integrity, and trustworthiness. Indeed, more reputable entities are presumably a better fit for most purposes. Thus, while reputation is a widespread notion in society, it is albeit an arguably ill-defined one. As a consequence, quantifyingreputationischallenging. Indeed, existingattemptstoquantifyreputation rely on either manual assessments or on a restrictive definition of reputation. Inthisthesis,insteadofrelyingonasingleandprecisedefinitionofreputation,we proposetoexploitthetransference ofreputationamongentitiesinordertoidentifythe most reputable ones. To this end, we introduce a conceptual framework of reputation flowsandproposeametricbasedonit, whichwecallP-score. Thisframeworkconsists of a random walk model that allows inferring the reputation of a target set of entities with respect to suitable sources of reputation. By using it, we can better understand how reputation flows between distinct entities in a reputation graph. Weinstantiateourmodelinanacademicsearchsettingtoaddressthreecommon ranking tasks namely, research group ranking, author ranking, and publication venue ranking. By relying on publishing behavior as a reputation signal, we demonstrate the effectiveness of our model in contrast to standard citation-based approaches for identifying reputable venues, authors, and research groups in the broad area of Computer Science. In addition, we demonstrate the robustness of our model to perturbations in the selection of reputation sources. Finally, we show that effective reputation sources can be chosen via the proposed model itself in a fully automatic fashion.
language:	por
metadata.dc.publisher.country:	Brasil
Publisher:	Universidade Federal de Minas Gerais
Publisher Initials:	UFMG
metadata.dc.publisher.department:	ICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO
metadata.dc.publisher.program:	Programa de Pós-Graduação em Ciência da Computação
Rights:	Acesso Aberto
URI:	http://hdl.handle.net/1843/31006
Issue Date:	6-Apr-2017
Appears in Collections:	Teses de Doutorado

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