提升PageRank公平性的高效边重排策略
Efficient Edge Rewiring Strategies for Enhancing PageRank Fairness
AI总结：
本文提出一种高效算法，通过重排网络边提升弱势群体的PageRank公平性，适用于大规模网络。
摘要：
我们研究了社交网络中的不公平现象，其中在男性主导行业中女性由于在网络中的不利位置而难以获得重要信息，例如招聘广告。我们调查了名为PageRank公平性的一种基于网络的公平性概念，该概念指的是对不同群体进行PageRank权重的公平分配。我们的目标是通过修改底层网络结构来提高PageRank公平性。更具体地说，我们研究了在允许重排固定数量边的情况下，如何最大化对弱势群体的PageRank公平性。基于贪心方法，我们利用根_spanning森林快速采样的技术，设计出一种有效的线性时间算法来解决这个问题。为了评估所提算法的准确性和性能，我们在各种真实世界网络数据上进行了大量实验。我们的实验表明，所提算法显著优于现有方法。我们的算法能够在几分钟内为拥有百万节点的网络生成准确的解决方案。
英文摘要：
We study the notion of unfairness in social networks, where a group such as females in a male-dominated industry are disadvantaged in access to important information, e.g. job posts, due to their less favorable positions in the network. We investigate a well-established network-based formulation of fairness called PageRank fairness, which refers to a fair allocation of the PageRank weights among distinct groups. Our goal is to enhance the PageRank fairness by modifying the underlying network structure. More precisely, we study the problem of maximizing PageRank fairness with respect to a disadvantaged group, when we are permitted to rewire a fixed number of edges in the network. Building on a greedy approach, we leverage techniques from fast sampling of rooted spanning forests to devise an effective linear-time algorithm for this problem. To evaluate the accuracy and performance of our proposed algorithm, we conduct a large set of experiments on various real-world network data. Our experiments demonstrate that the proposed algorithm significantly outperforms the existing ones. Our algorithm is capable of generating accurate solutions for networks of million nodes in just a few minutes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
