论文标题
公平意识的帕格兰克
Fairness-Aware PageRank
论文作者
论文摘要
在过去的几年中，算法公平引起了极大的关注。令人惊讶的是，网络中的公平性很少。在这项工作中，我们考虑了链接分析算法的公平性，尤其是著名的Pagerank算法。我们为公平提供定义，并提出了两种实现公平的方法。第一个修改了Pagerank算法的跳跃向量以使其公平，并且第二个节点施加了公平的行为。我们还考虑了达到公平性的问题，同时最大程度地减少了有关原始算法的效用损失。我们介绍了实际和合成图的实验，这些实验检查了Pagerank的公平性，并在定性和定量上证明了我们算法的特性。
Algorithmic fairness has attracted significant attention in the past years. Surprisingly, there is little work on fairness in networks. In this work, we consider fairness for link analysis algorithms and in particular for the celebrated PageRank algorithm. We provide definitions for fairness, and propose two approaches for achieving fairness. The first modifies the jump vector of the Pagerank algorithm to enfonce fairness, and the second imposes a fair behavior per node. We also consider the problem of achieving fairness while minimizing the utility loss with respect to the original algorithm. We present experiments with real and synthetic graphs that examine the fairness of Pagerank and demonstrate qualitatively and quantitatively the properties of our algorithms.
