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Cited by 265

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Dynamics of Opinion Forming in Structurally Balanced Social Networks

Open access
Altafini, Claudio·2012·PLoS ONE

Abstract

A structurally balanced social network is a social community that splits into two antagonistic factions (typical example being a two-party political system). The process of opinion forming on such a community is most often highly predictable, with polarized opinions reflecting the bipartition of the network. The aim of this paper is to suggest a class of dynamical systems, called monotone systems, as natural models for the dynamics of opinion forming on structurally balanced social networks. The high predictability of the outcome of a decision process is explained in terms of the order-preserving character of the solutions of this class of dynamical systems. If we represent a social network as a signed graph in which individuals are the nodes and the signs of the edges represent friendly or hostile relationships, then the property of structural balance corresponds to the social community being splittable into two antagonistic factions, each containing only friends.

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Metadata

Authors
Altafini, C.
Venue
PLoS ONE
Published
2012-06-19
Volume
7
Issue
6
Pages
e38135
ISSN
1932-6203 (ISSN-L)
OpenAlex
W1989544744
Cited by
265
References
1
FWCI
9.97
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Dynamics of Opinion Forming in Structurally Balanced Social Networks. https://app.cochat.ai/papers/10.1371/journal.pone.0038135
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Altafini, C. (2012). Dynamics of Opinion Forming in Structurally Balanced Social Networks. PLoS ONE. https://doi.org/10.1371/journal.pone.0038135

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.