RePBubLik：通过链路插入减小极化泡半径
RePBubLik: Reducing the Polarized Bubble Radius with Link Insertions
摘要：
在表达不同观点的页面之间的超链接图的拓扑结构可能会影响读者对不同内容的接触。结构性偏见可能会使读者陷入两极分化的气泡中，无法获得其他观点。我们将读者的行为建模为随机游走。如果从一个节点到一个不同观点的页面的随机游走的预期长度很大，那么这个节点就处于极化气泡中。图的结构偏差是高度极化气泡的半径之和。我们研究了通过边插入来减小结构偏差的问题。修复具有高极化气泡半径的所有节点很难在对数因子内近似，因此我们专注于找到插入的最佳$k$边缘，以最大限度地减少结构偏差。我们提出了RePBubLik，一个算法，利用随机游走接近中心的一个变种来选择插入的边缘。RePBubLik在温和的条件下得到了一个常数因子近似。它比现有的边推荐方法更快地减少了结构偏差，包括一些旨在减少图的极化的方法。
英文摘要：
The topology of the hyperlink graph among pages expressing different opinions may influence the exposure of readers to diverse content. Structural bias may trap a reader in a polarized bubble with no access to other opinions. We model readers' behavior as random walks. A node is in a polarized bubble if the expected length of a random walk from it to a page of different opinion is large. The structural bias of a graph is the sum of the radii of highly-polarized bubbles. We study the problem of decreasing the structural bias through edge insertions. Healing all nodes with high polarized bubble radius is hard to approximate within a logarithmic factor, so we focus on finding the best $k$ edges to insert to maximally reduce the structural bias. We present RePBubLik, an algorithm that leverages a variant of the random walk closeness centrality to select the edges to insert. RePBubLik obtains, under mild conditions, a constant-factor approximation. It reduces the structural bias faster than existing edge-recommendation methods, including some designed to reduce the polarization of a graph.
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