Random Walks on the Reputation Graph

Abstract

The identification of reputable entities is an important task in business, education, and many other fields. On the other hand, as an arguably subjective, multi-faceted concept, quantifying reputation is challenging. In this paper, instead of relying on a single, precise definition of reputation, we propose to exploit the transference of reputation among entities in order to identify the most reputable ones. To this end, we propose a novel random walk model to infer the reputation of a target set of entities with respect to suitable sources of reputation. We instantiate our model in an academic search setting, by modeling research groups as reputation sources and publication venues as reputation targets. 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 as well as researchers 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 semi-automatic fashion.