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：网络结构性偏见或隔离指的是网络中两个或多个不同群体高度内部连通但彼此间连接稀疏的情况。在许多场景中，增加不同群体间的连通性具有重要意义，例如减少社会摩擦或让个体接触多元观点。增加网络连通性的常用机制是在节点对间添加捷径边。在相关应用中，捷径边通常对应推荐内容（如推荐视频或新闻文章）。近年来已有文献研究通过捷径边减少结构性偏见或隔离的问题，其中随机游走已成为模拟基础网络导航与连通性的核心工具。现有方法要么无法提供近似保证，要么通过设计目标函数使其满足特定优化简化属性。本文研究在网络中添加给定数量捷径边以直接最小化两个不同群体间平均命中时间与最大命中时间的问题。针对平均命中时间最小化，我们提出基于超模性的贪婪双准则算法；而最大命中时间虽不具备超模性，但通过建立与平均命中时间及非对称k-中心问题的关联，我们同样为该目标开发了近似算法。