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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0267886 |
| _version_ | 1811290324733001728 |
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| author | Cassie McMillan Diane Felmlee James R Ashford |
| author_facet | Cassie McMillan Diane Felmlee James R Ashford |
| author_sort | Cassie McMillan |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T04:10:51Z |
| format | Article |
| id | doaj.art-417fa24ddb964da99101c592faf9c783 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T04:10:51Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-417fa24ddb964da99101c592faf9c7832022-12-22T03:03:07ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01175e026788610.1371/journal.pone.0267886Reciprocity, transitivity, and skew: Comparing local structure in 40 positive and negative social networks.Cassie McMillanDiane FelmleeJames R AshfordWhile 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.https://doi.org/10.1371/journal.pone.0267886 |
| spellingShingle | Cassie McMillan Diane Felmlee James R Ashford Reciprocity, transitivity, and skew: Comparing local structure in 40 positive and negative social networks. PLoS ONE |
| 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 |
| url | https://doi.org/10.1371/journal.pone.0267886 |
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