Measuring credibility of users in an e-learning environment

Abstract

Learning Villages (LV) is an E-learning platform for people's online discussions and frequently citing postings of one another. In this paper, we propose a novel method to rank credit authors in the LV system. We first propose a k-EACM graph to describe the article citation structure in the LV system. And then we build a weighted graph model k-UCM graph to reveal the implicit relationship between authors hidden behind the citations among their articles. Furthermore, we design a graph-based ranking algorithm, the Credit Author Ranking (CAR) algorithm, which can be applied to rank nodes in a graph with negative edges. Finally, we perform experimental evaluations by simulations. The results of evaluations illustrate that the proposed method works pretty well on ranking the credibility of users in the LV system.

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