Random walks on the reputation graph - Sabir Ribas (2017)
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.