In this paper, we investigate the conditions under which link analysis algorithms prevent minority groups from reaching high ranking slots. We find that the most common link-based algorithms using centrality metrics, such as PageRank and HITS, can reproduce and even amplify bias against minority groups in networks. Yet, their behavior differs: one one hand, we empirically show that PageRank mirrors the degree distribution for most of the ranking positions and it can equalize representation of minorities among the top ranked nodes; on the other hand, we find that HITS amplifies pre-existing bias in homophilic networks through a novel theoretical analysis, supported by empirical results. We find the root cause of bias amplification in HITS to be the level of homophily present in the network, modeled through an evolving network model with two communities. We illustrate our theoretical analysis on both synthetic and real datasets and we present directions for future work.

翻译：本文研究了链接分析算法在何种条件下会阻碍少数群体获得高排名位置。我们发现，最常用的基于中心性指标的链接算法（如PageRank和HITS）会在网络中再现甚至放大对少数群体的偏见。然而，两者的行为存在差异：一方面，实验表明PageRank在大部分排名位置上镜像度分布，并能均衡少数群体在顶部排名节点中的代表性；另一方面，通过新颖的理论分析及实证结果支持，我们发现HITS会放大同质网络中已有的偏见。我们通过双社区演化网络模型揭示，HITS中偏见放大的根本原因在于网络中的同质性水平。我们在合成数据集和真实数据集上验证了理论分析，并提出了未来研究方向。