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Reciprocity, transitivity, and skew: Comparing local structure in 40 positive and negative social networks
While most social network research focuses on positive relational ties, such as friendship and information exchange, scholars are beginning to examine the dark side of human interaction, where negative connections represent different forms of interpersonal conflict, intolerance, and abuse. Despite t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9122197/ https://www.ncbi.nlm.nih.gov/pubmed/35594268 http://dx.doi.org/10.1371/journal.pone.0267886 |
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author | McMillan, Cassie Felmlee, Diane Ashford, James R. |
author_facet | McMillan, Cassie Felmlee, Diane Ashford, James R. |
author_sort | McMillan, Cassie |
collection | PubMed |
description | While most social network research focuses on positive relational ties, such as friendship and information exchange, scholars are beginning to examine the dark side of human interaction, where negative connections represent different forms of interpersonal conflict, intolerance, and abuse. Despite this recent work, the extent to which positive and negative social network structure differs remains unclear. The current project considers whether a network’s small-scale, structural patterns of reciprocity, transitivity, and skew, or its “structural signature,” can distinguish positive versus negative links. Using exponential random graph models (ERGMs), we examine these differences across a sample of twenty distinct, negative networks and generate comparisons with a related set of twenty positive graphs. Relational ties represent multiple types of interaction such as like versus dislike in groups of adults, friendship versus cyberaggression among adolescents, and agreements versus disputes in online interaction. We find that both positive and negative networks contain more reciprocated dyads than expected by random chance. At the same time, patterns of transitivity define positive but not negative graphs, and negative networks tend to exhibit heavily skewed degree distributions. Given the unique structural signatures of many negative graphs, our results highlight the need for further theoretical and empirical research on the patterns of harmful interaction. |
format | Online Article Text |
id | pubmed-9122197 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-91221972022-05-21 Reciprocity, transitivity, and skew: Comparing local structure in 40 positive and negative social networks McMillan, Cassie Felmlee, Diane Ashford, James R. PLoS One Research Article While most social network research focuses on positive relational ties, such as friendship and information exchange, scholars are beginning to examine the dark side of human interaction, where negative connections represent different forms of interpersonal conflict, intolerance, and abuse. Despite this recent work, the extent to which positive and negative social network structure differs remains unclear. The current project considers whether a network’s small-scale, structural patterns of reciprocity, transitivity, and skew, or its “structural signature,” can distinguish positive versus negative links. Using exponential random graph models (ERGMs), we examine these differences across a sample of twenty distinct, negative networks and generate comparisons with a related set of twenty positive graphs. Relational ties represent multiple types of interaction such as like versus dislike in groups of adults, friendship versus cyberaggression among adolescents, and agreements versus disputes in online interaction. We find that both positive and negative networks contain more reciprocated dyads than expected by random chance. At the same time, patterns of transitivity define positive but not negative graphs, and negative networks tend to exhibit heavily skewed degree distributions. Given the unique structural signatures of many negative graphs, our results highlight the need for further theoretical and empirical research on the patterns of harmful interaction. Public Library of Science 2022-05-20 /pmc/articles/PMC9122197/ /pubmed/35594268 http://dx.doi.org/10.1371/journal.pone.0267886 Text en © 2022 McMillan et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article McMillan, Cassie Felmlee, Diane Ashford, James R. Reciprocity, transitivity, and skew: Comparing local structure in 40 positive and negative social networks |
title | Reciprocity, transitivity, and skew: Comparing local structure in 40 positive and negative social networks |
title_full | Reciprocity, transitivity, and skew: Comparing local structure in 40 positive and negative social networks |
title_fullStr | Reciprocity, transitivity, and skew: Comparing local structure in 40 positive and negative social networks |
title_full_unstemmed | Reciprocity, transitivity, and skew: Comparing local structure in 40 positive and negative social networks |
title_short | Reciprocity, transitivity, and skew: Comparing local structure in 40 positive and negative social networks |
title_sort | reciprocity, transitivity, and skew: comparing local structure in 40 positive and negative social networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9122197/ https://www.ncbi.nlm.nih.gov/pubmed/35594268 http://dx.doi.org/10.1371/journal.pone.0267886 |
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