An empirical investigation of network polarization

Interian, Ruben; Ribeiro, Celso C.

doi:10.1016/j.amc.2018.07.066

Cited by 14 publications

(17 citation statements)

References 10 publications

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“…The study concludes that the most effective instrument to combat disinformation-that is, false information spread deliberately to deceive-is to provide (or even "to flood") users of the platform with high-quality information, to ensure that the public also receives accurate, scientific or simply plural information when watching videos on some subject. Interian and Ribeiro (2018) have shown that many case-study real-world networks are extremely polarized. A polarized network is one divided into two or more strongly connected groups, with few edges between vertices belonging to different groups.…”

Section: Motivationmentioning

confidence: 99%

“…Furthermore, we also assume that one specific polarized group or even a collection of polarized groups in G have been previously detected by an external algorithm, as in Figs. 1 and 2 and in other real-life instances presented, for example, in Interian and Ribeiro (2018). Different polarization measures are discussed and compared, for example, by Interian and Ribeiro (2018) and can be used for the identification of the polarized groups.…”

Section: Basic Assumptionsmentioning

confidence: 99%

“…1 and 2 and in other real-life instances presented, for example, in Interian and Ribeiro (2018). Different polarization measures are discussed and compared, for example, by Interian and Ribeiro (2018) and can be used for the identification of the polarized groups. Alternatively, there are a number of excellent community detection methods in the literature that can detect such polarized groups, including, for example, the label propagation algorithm (Raghavan et al, 2007;Speriosu et al, 2011).…”

Section: Basic Assumptionsmentioning

confidence: 99%

“…Interian and Ribeiro (2018) have shown that many case‐study real‐world networks are extremely polarized. A polarized network is one divided into two or more strongly connected groups, with few edges between vertices belonging to different groups.…”

Section: Motivationmentioning

confidence: 99%

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Polarization reduction by minimum‐cardinality edge additions: Complexity and integer programming approaches

Interian

Moreno

Ribeiro

2020

Int Trans Operational Res

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Real-world networks are often extremely polarized because the communication between different groups of vertices can be weak and, most of the time, only vertices within the same group or sharing the same beliefs communicate to each other. In this work, we introduce the minimum-cardinality edge addition problem (MinCEAP) as a strategy for reducing polarization in real-world networks based on a principle of minimum external interventions. We present the problem formulation and discuss its complexity, showing that its decision version is NP-complete. We also propose three integer programming formulations for the problem and discuss computational results on artificially generated and real-life instances. Randomly generated instances with up to 1000 vertices are solved to optimality. On the real-life instances, we show that polarization can be reduced to the desired threshold with the addition of a few edges. The minimum intervention principle and the methods developed in this work are shown to constitute an effective strategy for tackling polarization issues in practice in social, interaction, and communication networks, which is a relevant problem in a world characterized by extreme political and ideological polarization.

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Section: Motivationmentioning

confidence: 99%

Section: Basic Assumptionsmentioning

confidence: 99%

Section: Basic Assumptionsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

See 2 more Smart Citations

Polarization reduction by minimum‐cardinality edge additions: Complexity and integer programming approaches

Interian

Moreno

Ribeiro

2020

Int Trans Operational Res

Self Cite

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“…In section III, we illustrate the potential of this measure by evaluating empirical networks. There have been many efforts to understand the phenomena of social ideological polarization [10][11][12], that is, when a community splits in two groups with different opinions about one or more issues. Our results suggest that individual opinion in a polarized network are strongly influenced by the whole networks topology.…”

Section: Introductionmentioning

confidence: 99%

Information parity in complex networks

Viol¹,

Vuksanovic²,

Hövel³

2019

Preprint

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A growing interest in complex networks theory results in an ongoing demand for new analytical tools. We propose a novel measure based on information theory that provides a new perspective for a better understanding of networked systems: Termed "information parity," it quantifies the consonance of influence among nodes with respect to the whole network architecture. Considering the statistics of geodesic distances, information parity detects how similarly a pair of nodes can influence and be influenced by the network.This allows us to quantify the quality of information gathered by the nodes. To demonstrate the method's potential, we evaluate a social network and human brain networks. Our results indicate that emerging phenomena like an ideological orientation of nodes in social network is severely influenced by their information parities. We also show that anatomical brain networks have a greater information parity in inter-hemispheric homologous regions placed near the midsagittal plane. Finally, functional networks have, on average, greater information parity for inter-hemispheric homologous regions in comparison to the whole network. We find that a pair of regions with high information parity exhibits higher correlation, suggesting that the functional correlations between cortical regions can be partially explained by the symmetry of their overall influences of the whole brain.

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Agent‐based modeling approach for group polarization behavior considering conformity and network relationship strength

Zhang

Wang

Chen

et al. 2020

Concurrency and Computation

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SummaryCurrently, group behaviors happen frequently with the development of network technology. As a typical social group behavior, group polarization has been attracted more and more academic attention due to its significant disturbance to public's daily lives. At present, the classic J‐A (proposed by Jager and Amblard) and D‐W (proposed by Deffuant and Weisbuch) models are used to analyze group polarization process. However, the main shortcomings existing in these models are that the individuals' psychology and their network relationships are rarely considered. In order to overcome the limitations, this article integrates the influence factors such as conformity and network relationship strength integrated into the polarization model. Besides, the BA (proposed by Barabasi and Albert) network model is used as the agent adjacency model due to its closer to the real social network structure. Subsequently, the experimental simulations are carried out with the multi‐agent Monte‐Carlo method so as to testify the efficiency and effectiveness. The results indicate that different information interaction modes have essential influence on group attitude polarization. Moreover, conformity parameters and the intensity of relationship have dual impacts on both speeding up and slowing down the polarization process.

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An empirical investigation of network polarization

Cited by 14 publications

References 10 publications

Polarization reduction by minimum‐cardinality edge additions: Complexity and integer programming approaches

Polarization reduction by minimum‐cardinality edge additions: Complexity and integer programming approaches

Information parity in complex networks

Agent‐based modeling approach for group polarization behavior considering conformity and network relationship strength

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