Vertical organizations, flat networks: Centrality and criminal collaboration in the Italian-American Mafia

Krajewski, Andrew T.; DellaPosta, Daniel; Felmlee, Diane

doi:10.1016/j.socnet.2021.06.001

Cited by 5 publications

(1 citation statement)

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“…The dearth of these configurations in negative graphs is consistent with balance theory predictions, which suggest that triads consisting of three negative ties should be unstable and underrepresented. The lack of transitivity in certain negative networks also could represent hierarchical patterns in which high ranking actors direct action only to those one rank below them, and allow their "minions" to interact among lower status individuals (e.g., Mafia networks, [58]). In addition, these patterns could be due to the volatility of these harmful links.…”

Section: Plos Onementioning

confidence: 99%

Reciprocity, transitivity, and skew: Comparing local structure in 40 positive and negative social networks

2022

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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.

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Section: Plos Onementioning

confidence: 99%