RePBubLik: Reducing the Polarized Bubble Radius with Link Insertions

TL;DR

RePBubLik is an algorithm designed to insert a limited number of edges into a hyperlink graph to effectively reduce structural bias and polarized bubbles, thereby increasing exposure to diverse opinions.

Contribution

The paper introduces RePBubLik, a novel algorithm that approximates optimal edge insertions to minimize polarization in opinion graphs, outperforming existing methods.

Findings

RePBubLik achieves a constant-factor approximation under mild conditions.

It reduces structural bias faster than existing edge-recommendation algorithms.

The method effectively decreases the polarized bubble radius in opinion graphs.

Abstract

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.