Exploiting heterogeneous scientific literature networks to combat ranking bias: Evidence from the computational linguistics area

It is important to help researchers find valuable papers from a large literature collection. To this end, many graph‐based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias . Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph‐based ranking algorithm, M utual R ank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less‐biased ranking than previous methods. M utual R ank provides a unified model that involves both intra‐ and inter‐network information for ranking papers, researchers, and venues simultaneously. We use the ACL A nthology N etwork as the benchmark data set and construct the gold standard from computer linguistics course websites of well‐known universities and two well‐known textbooks. The experimental results show that M utual R ank greatly outperforms the state‐of‐the‐art competitors, including P age R ank, HITS , C o R ank, F uture R ank, and P ‐ R ank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by M utual R ank are also quite reasonable.