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，使用中心性指标的最常见链接算法（如PageRank和HITS）能够复制甚至放大网络中对少数群体的偏见。然而，两者的行为存在差异：一方面，我们通过实证表明，PageRank的排名分布与度分布高度吻合，且能使少数群体在顶级节点中的代表性趋于均衡；另一方面，我们通过全新的理论分析（辅以实证结果）发现，HITS会放大同质网络中既存的偏见。我们通过一个包含两个社群演化的网络模型证明，HITS中偏见放大的根本原因在于网络内存在的同质性程度。我们通过合成数据集和真实数据集对理论分析进行了验证，并提出了未来研究方向。