Bridging the polarization gap: Maximizing diffusion among dissimilar communities - ANITI - Artificial and Natural Intelligence Toulouse Institute Access content directly
Journal Articles Transactions on Computational Collective Intelligence Year : 2022

Bridging the polarization gap: Maximizing diffusion among dissimilar communities

Abstract

Polarized networks, composed of weakly connected and self-reinforcing groups, can limit the diffusion of ideas, behaviors, and innovations. Here, we use a complex contagion model, in which diffusion depends on both the connectivity and the similarity of individuals, to ask how to optimally build bridges and enhance diffusion in networks characterized by fragmentation and homophily. First, we show that the problem is NP-hard. Then, we explore the space of solutions using heuristics, finding that connecting high degree nodes, or hubs, is an ineffective strategy to accelerate diffusion in fragmented and homophilous networks. We show that in these networks, diffusion is more effectively accelerated by connecting similar but low degree nodes. These results tell us that, in the presence of homophily and polarization, connecting communities through their most central actors may impede rather than facilitate diffusion. Instead, strategies to accelerate the diffusion of innovation, behaviors, and ideas should focus on creating links among the most similar members of different communities. These findings shed light on the diffusion of ideas and innovations in polarized networks. CCS Concepts: • Mathematics of computing → Network optimization; • Information systems → Social networks
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Dates and versions

hal-04050780 , version 1 (28-02-2024)

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Marcin Waniek, César Hidalgo. Bridging the polarization gap: Maximizing diffusion among dissimilar communities. Transactions on Computational Collective Intelligence, 2022, 1 (2), pp.263391372211285. ⟨10.1177/26339137221128542⟩. ⟨hal-04050780⟩

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