Connections (Toronto, Ont.)最新文献_第4页

The impact of contact tracing on the spread of COVID-19: an egocentric agent-based model 接触者追踪对COVID-19传播的影响:一个以自我为中心的agent模型

Connections (Toronto, Ont.) Pub Date : 2021-01-01 DOI: 10.21307/CONNECTIONS-2021.022

Andrew Pilny, Lin Xiang, C. Huber, Will R Silberman, Sean Goatley-Soan

{"title":"The impact of contact tracing on the spread of COVID-19: an egocentric agent-based model","authors":"Andrew Pilny, Lin Xiang, C. Huber, Will R Silberman, Sean Goatley-Soan","doi":"10.21307/CONNECTIONS-2021.022","DOIUrl":"https://doi.org/10.21307/CONNECTIONS-2021.022","url":null,"abstract":"Abstract 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 R0), 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 R0.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"41 1","pages":"25 - 46"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67974625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chaos from order: a network analysis of in-fighting before and after El Chapo’s arrest 秩序中的混乱：El Chapo被捕前后战斗的网络分析

Connections (Toronto, Ont.) Pub Date : 2021-01-01 DOI: 10.21307/connections-2021.023

Darren Colby

{"title":"Chaos from order: a network analysis of in-fighting before and after El Chapo’s arrest","authors":"Darren Colby","doi":"10.21307/connections-2021.023","DOIUrl":"https://doi.org/10.21307/connections-2021.023","url":null,"abstract":"Abstract 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.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"41 1","pages":"1 - 11"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43703985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Social Cohesion and Cooperation for Public Goods 社会凝聚力与公共产品合作

Connections (Toronto, Ont.) Pub Date : 2021-01-01 DOI: 10.21307/CONNECTIONS-2019.020

Jeroen Bruggeman, R. Corten

引用次数: 1

Commentary: How to do personal network surveys: from name generators to statistical modeling 评论:如何做个人网络调查:从名字生成器到统计建模

Connections (Toronto, Ont.) Pub Date : 2020-01-01 DOI: 10.21307/connections-2019.015

I. Maya-Jariego

引用次数: 2

Embarked on social processes (the rivers) in dynamic and multilevel networks (the boats) 在动态和多层次的网络（船只）中开始社会进程（河流）

Connections (Toronto, Ont.) Pub Date : 2020-01-01 DOI: 10.21307/connections-2019.013

E. Lazega

引用次数: 0

Clients’ outcomes from providers’ networks: the role of relational exclusivity and complementary capabilities 客户从供应商网络中获得的结果:关系排他性和互补能力的作用

Connections (Toronto, Ont.) Pub Date : 2020-01-01 DOI: 10.21307/connections-2019.011

Denis Trapido, F. Pallotti, A. Lomi

引用次数: 0

COVID-19 Health Communication Networks on Twitter: Identifying Sources, Disseminators, and Brokers 推特上的新冠肺炎健康传播网络：识别来源、传播者和经纪人

Connections (Toronto, Ont.) Pub Date : 2020-01-01 DOI: 10.21307/connections-2019.018

Ian Kim, T. Valente

{"title":"COVID-19 Health Communication Networks on Twitter: Identifying Sources, Disseminators, and Brokers","authors":"Ian Kim, T. Valente","doi":"10.21307/connections-2019.018","DOIUrl":"https://doi.org/10.21307/connections-2019.018","url":null,"abstract":"Abstract Coronavirus disease of 2019 (COVID-19)’s devastating effects on the physical and mental health of the public are unlike previous medical crises, in part because of people’s collective access to communication technologies. Unfortunately, a clear understanding of the diffusion of health information on social media is lacking, which has a potentially negative impact on the effectiveness of emergency communication. This study applied social network analysis approaches to examine patterns of #COVID19 information flow on Twitter. A total of 1,404,496 publicly available tweets from 946,940 U.S. users were retrieved and analyzed. Particular attention was paid to the structures of retweet and mention networks and identification of influential users: information sources, disseminators, and brokers. Overall, COVID-19 information was not transmitted efficiently. Findings pointed to the importance of fostering connections between clusters to promote the diffusion in both networks. Lots of localized clusters limited the spread of timely information, causing difficulty in establishing any momentum in shaping urgent public actions. Rather than health and communication professionals, there was dominant involvement of non-professional users responsible for major COVID-19 information generation and dissemination, suggesting a lack of credibility and accuracy in the information. Inadequate influence of health officials and government agencies in brokering information contributed to concerns about the spread of dis/misinformation to the public. Significant differences in the type of influential users existed across roles and across networks. Conceptual and practical implications for emergency communication strategies are discussed.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"40 1","pages":"129 - 142"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41688213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Hairball Buster: A Graph Triage Method for Viewing and Comparing Graphs 毛球终结者:查看和比较图形的图形分类方法

Connections (Toronto, Ont.) Pub Date : 2020-01-01 DOI: 10.21307/CONNECTIONS-2019.009

Patrick D. Allen, Mark Alan Matties, Elisha Peterson

{"title":"Hairball Buster: A Graph Triage Method for Viewing and Comparing Graphs","authors":"Patrick D. Allen, Mark Alan Matties, Elisha Peterson","doi":"10.21307/CONNECTIONS-2019.009","DOIUrl":"https://doi.org/10.21307/CONNECTIONS-2019.009","url":null,"abstract":"Abstract Hairball buster (HB) (also called node-neighbor centrality or NNC) is an approach to graph analytic triage that uses simple calculations and visualization to quickly understand and compare graphs. Rather than displaying highly interconnected graphs as ‘hairballs’ that are difficult to understand, HB provides a simple standard visual representation of a graph and its metrics, combining a monotonically decreasing curve of node metrics with indicators of each node’s neighbors’ metrics. The HB visual is canonical, in the sense that it provides a standard output for each node-link graph. It helps analysts quickly identify areas for further investigation, and also allows for easy comparison between graphs of different data sets. The calculations required for creating an HB display is order M plus N log N, where N is the number of nodes and M is the number of edges. This paper includes examples of the HB approach applied to four real-world data sets. It also compares HB to similar visual approaches such as degree histograms, adjacency matrices, blockmodeling, and force-based layout techniques. HB presents greater information density than other algorithms at lower or equal calculation cost, efficiently presenting information in a single display that is not available in any other single display.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"40 1","pages":"1 - 24"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45076101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Visualizing Multilevel Networks for the Analysis of Superposed Levels of Collective Agency 可视化多层网络分析集体代理的叠加层次

Connections (Toronto, Ont.) Pub Date : 2020-01-01 DOI: 10.21307/CONNECTIONS-2019.019

E. Lazega

引用次数: 1

Academic Collaboration via Resource Contributions: An Egocentric Dataset 通过资源贡献的学术合作:一个以自我为中心的数据集

Connections (Toronto, Ont.) Pub Date : 2020-01-01 DOI: 10.21307/CONNECTIONS-2019.010

M. Bojanowski, Dominika Czerniawska, Wojciech Fenrich

引用次数: 2