  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.
