<abstract xmlns="http://www.w3.org/1999/xhtml">

A cohesive network keeps groups together and enables members to communicate about and cooperate for public goods. For ongoing cooperation, group members have to know if their group members cooperate or defect, but this information—mostly through gossip—is threatened by noise and biases. If there are redundant information channels, however, errors in monitoring and transmission in one imperfect channel can, to some degree, be corrected by information through another imperfect channel, and may lead to higher levels of cooperation. An influential conceptualization of social cohesion based on redundancy is K-connectivity: the minimum number (K) of node-independent paths connecting pairs of nodes in a group’s network. In a lab experiment, we tested if higher K-connectivity yields higher levels of cooperation for public goods, controlling for a number of other network effects such as density, size, and average distance. We do not find the hypothesized effect, which might be due to a not-earlier-found shortcoming of the concept, and we propose a solution.
</abstract>

Social Cohesion and Cooperation for Public Goods

<abstract xmlns="http://www.w3.org/1999/xhtml">

In this paper, we introduce a straightforward method to measure organizational diversity that utilizes the importance of social ties. In developing our single-mode brokerage-based measure of diversity, two themes are central. The first stems from the work that has emphasized the benefits of increased diversity for organizational decision-making, while the second draws on the insights from social network analyses to consider the roles that individuals play in connecting individuals not already affiliated. The paper is organized into three sections: the first contextualizes our measure within the work that has theorized the importance of brokerage, homophily, and organizational diversity; the second describes and outlines the steps for deconstructing brokerage roles that is necessary to generate our measure of organizational diversity; and the third contributes to the literature by applying our brokerage-based measures of diversity to an exploration of whether increased levels of demographic diversity is related to greater corporate responsibility in 2005 as indicated by lower numbers of regulatory violations among the Fortune 100.
</abstract>

A brokerage-based measure of organizational diversity and exploratory analysis of regulatory violations among Fortune 100 corporations

<abstract xmlns="http://www.w3.org/1999/xhtml">

At its core, contact tracing is a form of egocentric network analysis (ENA). One of the biggest obstacles for ENA is informant accuracy (i.e., amount of true contacts identified), which is even more prominent for interaction-based network ties because they often represent episodic relational events, rather than enduring relational states. This research examines the effect of informant accuracy on the spread of COVID-19 through an egocentric, agent-based model. Overall when the average person transmits COVID-19 to 1.62 other people (i.e., the <italic>R</italic>0), they must be, on average, 75% accurate with naming their contacts. In higher transmission contexts (i.e., transmitting to at least two other people), the results show that multi-level tracing (i.e., contact tracing the contacts) is the only viable strategy. Finally, sensitivity analysis shows that the effectiveness of contact tracing is negatively impacted by the timing and overall percent of asymptomatic cases. Overall, the results suggest that if contact tracing is to be effective, it must be fast, accurate, and accompanied by other interventions like mask-wearing to drive down the average <italic>R</italic>0.
</abstract>

The impact of contact tracing on the spread of COVID-19: an egocentric agent-based model

<abstract xmlns="http://www.w3.org/1999/xhtml">

The effect of leadership decapitation—the capture or killing of the leader of an armed group—on future violence has been studied with competing conclusions. In Mexico, leadership decapitation has been found to increase violence and in-fighting among drug cartels. However, the causal pathways between leadership decapitation and in-fighting are unclear. In this article, it is hypothesized that leadership decapitation will weaken alliances between armed actors, lead to greater preferential attachment in networks of cartels and militias, and result in greater transitive closure as cartels seek to expand their power. These hypotheses are tested with a stochastic actor oriented model on a network dataset of episodes of infighting among cartels and the militias formed to opposed them between the five years before and after Joaquín, “El Chapo” Guzmán Loera, the former leader of the Sinaloa Cartel, was arrested in 2016. The results show that alliances have virtually no effect on the decision of cartels and militias to fight each other; weaker organizations faced a higher reputational cost after El Chapo’s detention; and post-arrest cartel in-fighting did not increase as a result of uncertainty about the relative balance of power among cartels.
</abstract>

Chaos from order: a network analysis of in-fighting before and after El Chapo’s arrest

AHEAD OF PRINT

Volume 44 (2024): Issue 1 (January 2024)

Volume 43 (2023): Issue 1 (January 2023)

Volume 42 (2022): Issue 1 (January 2022)

Volume 41 (2021): Issue 1 (January 2021)

Volume 40 (2020): Issue 1 (January 2020)

Volume 39 (2019): Issue 1 (January 2019)

Volume 38 (2018): Issue 1 (January 2018)

Volume 37 (2017): Issue 1-2 (January 2017)

Volume 36 (2016): Issue 1 (January 2016)

Connections

Connections publishes original empirical, theoretical, and methodological articles, as well as critical reviews dealing with applications of social network analysis. From volume 2018 the journal is published in a continous format.  The research spans many disciplines and domains including: Anthropology, Sociology, Psychology, Communication, Economics, Organizational Behavior, Knowledge Management, Marketing, Social Psychology, Mathematics, Public Health, Medicine, Computer Science, and Policy.  As an official journal of the International Network for Social Network Analysis, the emphasis of the publication is to reflect the ever-growing and continually expanding community of scholars using network analytic techniques. Connections also provides an outlet for sharing news about social network concepts and techniques and new tools for research.  Rejection rate: 60%  Open Access Policy  This journal provides immediate open access to its content under the Creative Commons CC BY 4.0 license on the principle that making research freely available to the public supports a greater global exchange of knowledge. Under the Creative Commons CC BY 4.0 license users are free to share (copy and redistribute the material in any medium or format) and adapt the work (remix, transform, and build upon the material for any purpose, even commercially) if the contribution was properly attributed and all of the changes indicated.  ABOUT SOCIETY  International Network for Social Network Analysis is the professional association for researchers interested in social network analysis. The association is a non-profit organization incorporated in the state of Delaware and founded by Barry Wellman in 1977.  INSNA was founded on the premise that the behavior and lives of social entities are affected by their position in the overall social structure. By examining the etiology and consequences of structural forms overall, of the location of entities within these structures, and of the formation and dynamics of ties that make up these structures, INSNA hopes to learn about the parts of behavior that are uniquely social.  Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process.