Structural bias or segregation of networks refers to situations where two or more disparate groups are present in the network, so that the groups are highly connected internally, but loosely connected to each other. In many cases it is of interest to increase the connectivity of disparate groups so as to, e.g., minimize social friction, or expose individuals to diverse viewpoints. A commonly-used mechanism for increasing the network connectivity is to add edge shortcuts between pairs of nodes. In many applications of interest, edge shortcuts typically translate to recommendations, e.g., what video to watch, or what news article to read next. The problem of reducing structural bias or segregation via edge shortcuts has recently been studied in the literature, and random walks have been an essential tool for modeling navigation and connectivity in the underlying networks. Existing methods, however, either do not offer approximation guarantees, or engineer the objective so that it satisfies certain desirable properties that simplify the optimization~task. In this paper we address the problem of adding a given number of shortcut edges in the network so as to directly minimize the average hitting time and the maximum hitting time between two disparate groups. Our algorithm for minimizing average hitting time is a greedy bicriteria that relies on supermodularity. In contrast, maximum hitting time is not supermodular. Despite, we develop an approximation algorithm for that objective as well, by leveraging connections with average hitting time and the asymmetric k-center problem.

翻译：网络的结构性偏差或隔离指的是网络中存在的两个或多个不同群体，这些群体内部连接紧密，但彼此之间连接稀疏。在许多情况下，增加不同群体间的连通性是有意义的，例如，减少社会摩擦或让个体接触多元观点。一种常用的增加网络连通性的机制是在节点对之间添加捷径边。在许多相关应用中，捷径边通常对应推荐，例如推荐观看的视频或阅读的新闻文章。近年来，文献中研究了通过捷径边减少结构性偏差或隔离的问题，而随机游走已成为建模底层网络中导航和连通性的重要工具。然而，现有方法要么不提供近似保证，要么设计目标函数使其满足某些理想性质以简化优化任务。本文研究在网络中添加给定数量的捷径边，以直接最小化两个不同群体间的平均命中时间和最大命中时间的问题。我们的平均命中时间最小化算法是一种依赖超模性的贪婪双准则方法。相比之下，最大命中时间不具有超模性。尽管如此，我们通过利用其与平均命中时间以及非对称k中心问题的联系，也为该目标开发了一种近似算法。