· 這里是JCS編輯部
JCS Focus
本周 JCS Focus
將為大家推送
Social Networks
(《社會網(wǎng)絡(luò)》)
的最新目錄與摘要
期刊簡介
Social Networks(《社會網(wǎng)絡(luò)》)創(chuàng)刊于1979年，目前由Elsevier出版。該刊2023年的影響因子為3.1，在人類學(xué)、社會學(xué)等多個學(xué)科的期刊排名中均位列Q1區(qū)。
Social Networks主要關(guān)注社會網(wǎng)絡(luò)相關(guān)的理論與經(jīng)驗研究。該刊旨在為人類學(xué)、社會學(xué)、歷史學(xué)、社會心理學(xué)、政治學(xué)、人文地理學(xué)、生物學(xué)、經(jīng)濟學(xué)、傳播學(xué)等學(xué)科的研究者提供一個學(xué)術(shù)交流的平臺，聚焦探討社會關(guān)系的經(jīng)驗性結(jié)構(gòu)的各類研究，也發(fā)表理論、方法論以及關(guān)于社交網(wǎng)絡(luò)和社會結(jié)構(gòu)的書評。
Social Networks每年發(fā)布四期，最新一期(Volume 81 May 2025)共5篇文章，均可免費獲取，詳情如下（點擊文末“閱讀原文”即可直達該刊官網(wǎng)）。
原版目錄
ARTICLES
# ARTICLE 1
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.
# ARTICLE 2
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.
# ARTICLE 3
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.
# ARTICLE 4
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
# ARTICLE 5
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.
以上就是本期 JCS Focus 的全部內(nèi)容啦
期刊/趣文/熱點/漫談
學(xué)術(shù)路上，
JCS 陪你一起成長！
JCS
《中國社會學(xué)學(xué)刊》（The Journal of Chinese Sociology）于2014年10月由中國社會科學(xué)院社會學(xué)研究所創(chuàng)辦。作為中國大陸第一本英文社會學(xué)學(xué)術(shù)期刊，JCS致力于為中國社會學(xué)者與國外同行的學(xué)術(shù)交流和合作打造國際一流的學(xué)術(shù)平臺。JCS由全球最大科技期刊出版集團施普林格·自然(Springer Nature)出版發(fā)行，由國內(nèi)外頂尖社會學(xué)家組成強大編委會隊伍，采用雙向匿名評審方式和“開放獲取”(open access)出版模式。JCS已于2021年5月被ESCI收錄。2022年，JCS的CiteScore分值為2.0（Q2），在社科類別的262種期刊中排名第94位，位列同類期刊前36%。2023年，JCS在科睿唯安發(fā)布的2023年度《期刊引證報告》（JCR）中首次獲得影響因子并達到1.5（Q3）。
歡迎向《中國社會學(xué)學(xué)刊》投稿!!
Please consider submitting to The Journal of Chinese Sociology!!
官方網(wǎng)站：
https://journalofchinesesociology.springeropen.com
熱門跟貼