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JCS Focus

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ARTICLES

Why distinctiveness centrality is distinctive

Andrea Fronzetti Colladon, Maurizio Naldi

This paper responds to a commentary by Neal (2024) regarding the Distinctiveness centrality metrics introduced by Fronzetti Colladon and Naldi (2020). Distinctiveness centrality offers a novel reinterpretation of degree centrality, particularly emphasizing the significance of direct connections to loosely connected peers within (social) networks. This response paper presents a more comprehensive analysis of the correlation between Distinctiveness and the Beta and Gamma measures. All five Distinctiveness measures are considered, as well as a more meaningful range of the α parameter and different network topologies, distinguishing between weighted and unweighted networks. Findings indicate significant variability in correlations, supporting the viability of Distinctiveness as alternative or complementary metrics within social network analysis. Moreover, the paper presents computational complexity analysis and simplified R code for practical implementation. Encouraging initial findings suggest potential applications in diverse domains, inviting further exploration and comparative analyses.

Estimating policy effects in a social network with independent set sampling

Eugene T.Y. Ang, Prasanta Bhattacharya, Andrew E.B. Lim

Evaluating the impact of policy interventions on respondents who are embedded in a social network is often challenging due to the presence of network interference within the treatment groups, as well as between treatment and non-treatment groups. In this paper, we propose a novel empirical strategy that combines network sampling based on the identification of independent sets with a stochastic actor-oriented model (SAOM) to infer the direct and net effects of a policy. By assigning respondents from an independent set to the treatment, we are able to block direct spillover of the treatment among the treated respondents for an extended period of time, during which the direct effect of the treatment can be isolated from the associated network interference. We empirically demonstrate this using a simulation-based evaluation of a fictitious policy implementation using both real-life and generated networks, and use a counterfactual approach to estimate the treatment effect of the policy. Our results highlight the effectiveness of our proposed empirical strategy, and notably, the role of network sampling techniques in influencing the evaluation of policy effects. The findings from this study have the potential to help researchers and policymakers with planning, designing, and anticipating policy responses in a networked society.

Revising the Borgatti-Everett core-periphery model: Inter-categorical density blocks and partially connected cores

José Luis Estévez, Carl Nordlund

Borgatti and Everett's model (2000) remains the prevailing standard for identifying categorical core-periphery structures in empirical networks, yet this method poses two significant issues. The first concerns the handling of inter-categorical ties—those linking core and periphery actors. The second problem is the model's definition of the ideal core as a complete block or clique, which can be overly stringent in practical applications. Building on advancements in direct blockmodeling, we propose modifications to address these shortcomings. To better handle inter-categorical ties, we replace the traditional cell-wise correlation approach with one based on exact- and minimum-density blocks. To relax the constraint of a fully connected core, we introduce the p-core, a proportional adaptation of the k-core/k-plex cohesive subgroups, providing greater flexibility in defining the level of cohesion required for core membership. We illustrate the advantages of these enhancements using both classic network examples and synthetic networks.

Corrigendum to “Impact of methods for reducing respondent burden on personal network structural measures” [Soc. Netw. 29 (2007) 300–315]

Christopher McCarty, Peter D. Killworth

Digital communication and tie formation amongst freshmen students during and after the pandemic

Judith Gilsbach, Johannes Stauder

This study examines the network evolution among sociology freshmen students during and after the Covid-19 pandemic as a natural experiment on the impacts of digitalised communication. The first surveyed cohort (N = 42) began their studies under lockdown in October 2020, when all classes were taught online (lockdown cohort). The second cohort (N = 66) started one year later when the lockdown measures were released partly and most classes were taught in a hybrid mode (hybrid cohort). We use Stochastic Actor-Oriented Models (SAOM) for model estimation; missing relations due to actor non-response are multiply imputed using SAOM-based procedures. The findings show (1) that the network among students of the lockdown cohort developed slower and reached a lower density at the end of the first term, (2) that the probability of triadic closure was significantly lower in the lockdown than in the hybrid cohort and (3) that in both cohorts, students have a stronger tendency to get acquainted if they share classes, but (4) that shared classes were more important for tie formation during lockdown. We conclude that digital communication will mitigate the opportunities to make new acquaintances and friends.