RePBubLik: Reducing the Polarized Bubble Radius with Link Insertions

The topology of the <PRE_TAG>hyperlink graph</POST_TAG> among pages expressing different opinions may influence the exposure of readers to diverse content. Structural bias may trap a reader in a <PRE_TAG>polarized bubble</POST_TAG> with no access to other opinions. We model readers' behavior as <PRE_TAG>random walks</POST_TAG>. A node is in a <PRE_TAG>polarized bubble</POST_TAG> 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-<PRE_TAG>polarized bubble</POST_TAG>s. We study the problem of decreasing the structural bias through edge insertions. Healing all nodes with high <PRE_TAG>polarized bubble</POST_TAG> 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.