RePBubLik: Reducing Polarized Bubble Radius with Link Insertions

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. CCS CONCEPTS • Theory of computation→ Random walks and Markov chains; Graph algorithms analysis; • Information systems→ Social networks; Social recommendation.

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