基于边重新加权的公平性PageRank算法
Fairness-aware PageRank via Edge Reweighting
AI总结：
本文提出基于边重新加权的公平性PageRank算法，通过优化转移矩阵实现公平性提升，无需修改网络拓扑。
摘要：
链接分析算法，如PageRank，通过评估个体顶点的重要性来理解网络的结构动态。最近，随着负责任的AI的重要性上升，链接分析算法中的公平性问题引起了关注。在本文中，我们提出了一种新的方法，通过重新加权基础转移矩阵中的转移概率，将群体公平性纳入PageRank算法中。我们通过最小化公平损失来实现公平的PageRank，公平损失是原始群体层面的PageRank分布与目标PageRank分布之间的差异。我们进一步定义了一个适应群体的公平性概念，该概念通过考虑每个群体的随机游走并带有群体偏好的重启来考虑群体同质性。由于公平损失是非凸的，我们提出了一种高效的投影梯度下降方法来计算局部最优的边权重。与先前的方法不同，我们不推荐在网络中添加新边，也不调整重启向量。相反，我们保持基础网络的拓扑结构不变，仅修改现有边的相对重要性。我们通过与最先进的基线进行实证比较，展示了我们方法的有效性，其中非常小的转移矩阵变化导致PageRank算法公平性显著提高。
英文摘要：
Link-analysis algorithms, such as PageRank, are instrumental in understanding the structural dynamics of networks by evaluating the importance of individual vertices based on their connectivity. Recently, with the rising importance of responsible AI, the question of fairness in link-analysis algorithms has gained traction. In this paper, we present a new approach for incorporating group fairness into the PageRank algorithm by reweighting the transition probabilities in the underlying transition matrix. We formulate the problem of achieving fair PageRank by seeking to minimize the fairness loss, which is the difference between the original group-wise PageRank distribution and a target PageRank distribution. We further define a group-adapted fairness notion, which accounts for group homophily by considering random walks with group-biased restart for each group. Since the fairness loss is non-convex, we propose an efficient projected gradient-descent method for computing locally-optimal edge weights. Unlike earlier approaches, we do not recommend adding new edges to the network, nor do we adjust the restart vector. Instead, we keep the topology of the underlying network unchanged and only modify the relative importance of existing edges. We empirically compare our approach with state-of-the-art baselines and demonstrate the efficacy of our method, where very small changes in the transition matrix lead to significant improvement in the fairness of the PageRank algorithm.
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。
