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The topology of the hyperlink graph among pages expressing different opinionsmay influence the exposure of readers to diverse content. Structural bias maytrap a reader in a polarized bubble with no access to other opinions. We modelreaders' behavior as random walks. A node is in a polarized bubble if theexpected length of a random walk from it to a page of different opinion islarge. The structural bias of a graph is the sum of the radii ofhighly-polarized bubbles. We study the problem of decreasing the structuralbias through edge insertions. Healing all nodes with high polarized bubbleradius is hard to approximate within a logarithmic factor, so we focus onfinding the best $k$ edges to insert to maximally reduce the structural bias.We present RePBubLik, an algorithm that leverages a variant of the random walkcloseness centrality to select the edges to insert. RePBubLik obtains, undermild conditions, a constant-factor approximation. It reduces the structuralbias faster than existing edge-recommendation methods, including some designedto reduce the polarization of a graph.

RePBubLik: Reducing the Polarized Bubble Radius with Link Insertions