Pub Date : 2018-04-25DOI: 10.21307/connections-2017-004
J. Antonio, Rivero Ostoic
Abstract This paper extends Compositional Equivalence – which is a structural correspondence type intended for multiplex networks – by incorporating actor attributes in the modeling of the network relational structure as diagonal matrices. As an illustration, we construct the positional system of the Florentine families network with Business and Marriage ties together with relevant characteristics acquired from the actors such as the families’ financial Wealth and the number of Priorates they held. Different representations of the cumulated person hierarchies reveal that adding Wealth to the modeling provides a more accurate picture of what the substantial narrative says about this network.
{"title":"Compositional Equivalence with Actor Attributes: Positional Analysis of the Florentine Families Network","authors":"J. Antonio, Rivero Ostoic","doi":"10.21307/connections-2017-004","DOIUrl":"https://doi.org/10.21307/connections-2017-004","url":null,"abstract":"Abstract This paper extends Compositional Equivalence – which is a structural correspondence type intended for multiplex networks – by incorporating actor attributes in the modeling of the network relational structure as diagonal matrices. As an illustration, we construct the positional system of the Florentine families network with Business and Marriage ties together with relevant characteristics acquired from the actors such as the families’ financial Wealth and the number of Priorates they held. Different representations of the cumulated person hierarchies reveal that adding Wealth to the modeling provides a more accurate picture of what the substantial narrative says about this network.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"37 1","pages":"53 - 68"},"PeriodicalIF":0.0,"publicationDate":"2018-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46202747","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}
Pub Date : 2018-01-01DOI: 10.21307/CONNECTIONS-2018-002
S. Borgatti, E. Quintane
Abstract This techniques guide provides a brief answer to the question: How to choose a dichotomization threshold? We propose a two step approach to selecting a dichotomization threshold. We illustrate the approaches using two datasets and provide instructions on how to perform these approaches in R and UCINET.
{"title":"Techniques: Dichotomizing a Network","authors":"S. Borgatti, E. Quintane","doi":"10.21307/CONNECTIONS-2018-002","DOIUrl":"https://doi.org/10.21307/CONNECTIONS-2018-002","url":null,"abstract":"Abstract This techniques guide provides a brief answer to the question: How to choose a dichotomization threshold? We propose a two step approach to selecting a dichotomization threshold. We illustrate the approaches using two datasets and provide instructions on how to perform these approaches in R and UCINET.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":" ","pages":"1 - 11"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47360636","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}
Pub Date : 2018-01-01DOI: 10.21307/CONNECTIONS-2018-003
Russ Bernard, E. Bienenstock, S. Borgatti, U. Brandes, R. Burt, C. Butts, P. Doreian, T. Fararo, Katie Faust, Jeff Johnson, A. Kanfer, D. Krackhardt, J. Skvoretz, B. Wellman
{"title":"A collection of tributes to Linton C. Freeman","authors":"Russ Bernard, E. Bienenstock, S. Borgatti, U. Brandes, R. Burt, C. Butts, P. Doreian, T. Fararo, Katie Faust, Jeff Johnson, A. Kanfer, D. Krackhardt, J. Skvoretz, B. Wellman","doi":"10.21307/CONNECTIONS-2018-003","DOIUrl":"https://doi.org/10.21307/CONNECTIONS-2018-003","url":null,"abstract":"","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"38 1","pages":"1 - 16"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45682587","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}
Pub Date : 2018-01-01DOI: 10.21307/CONNECTIONS-2018-001
Emily A. Knapp, U. Bilal, B. T. Burke, Geoff B. Dougherty, T. Glass
Abstract Network methods have been applied to obesity to map connections between obesity-related genes, model biological feedback mechanisms and potential interventions, and to understand the spread of obesity through social networks. However, network methods have not been applied to understanding the obesogenic environment. Here, we created a network of 32 features of communities hypothesized to be related to obesity. Data from an existing study of determinants of obesity among 1,288 communities in Pennsylvania were used. Spearman correlation coefficients were used to describe the bivariate association between each pair of features. These correlations were used to create a network in which the nodes are community features and weighted edges are the strength of the correlations among those nodes. Modules of clustered features were identified using the walktrap method. This network was plotted, and then examined separately for communities stratified by quartiles of child obesity prevalence. We also examined the relationship between measures of network centrality and child obesity prevalence. The overall structure of the network suggests that environmental features geographically co-occur, and features of the environment that were more highly correlated with body mass index were more central to the network. Three clusters were identified: a crime-related cluster, a food-environment and land use-related cluster, and a physical activity-related cluster. The structure of connections between features of the environment differed between communities with the highest and lowest burden of childhood obesity, and a higher degree of average correlation was observed in the heaviest communities. Network methods may help to explicate the concept of the obesogenic environment, and ultimately to illuminate features of the environment that may serve as levers of community-level intervention.
{"title":"A network approach to understanding obesogenic environments for children in Pennsylvania","authors":"Emily A. Knapp, U. Bilal, B. T. Burke, Geoff B. Dougherty, T. Glass","doi":"10.21307/CONNECTIONS-2018-001","DOIUrl":"https://doi.org/10.21307/CONNECTIONS-2018-001","url":null,"abstract":"Abstract Network methods have been applied to obesity to map connections between obesity-related genes, model biological feedback mechanisms and potential interventions, and to understand the spread of obesity through social networks. However, network methods have not been applied to understanding the obesogenic environment. Here, we created a network of 32 features of communities hypothesized to be related to obesity. Data from an existing study of determinants of obesity among 1,288 communities in Pennsylvania were used. Spearman correlation coefficients were used to describe the bivariate association between each pair of features. These correlations were used to create a network in which the nodes are community features and weighted edges are the strength of the correlations among those nodes. Modules of clustered features were identified using the walktrap method. This network was plotted, and then examined separately for communities stratified by quartiles of child obesity prevalence. We also examined the relationship between measures of network centrality and child obesity prevalence. The overall structure of the network suggests that environmental features geographically co-occur, and features of the environment that were more highly correlated with body mass index were more central to the network. Three clusters were identified: a crime-related cluster, a food-environment and land use-related cluster, and a physical activity-related cluster. The structure of connections between features of the environment differed between communities with the highest and lowest burden of childhood obesity, and a higher degree of average correlation was observed in the heaviest communities. Network methods may help to explicate the concept of the obesogenic environment, and ultimately to illuminate features of the environment that may serve as levers of community-level intervention.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"38 1","pages":"1 - 11"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44233369","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}
Pub Date : 2017-01-01DOI: 10.21307/CONNECTIONS-2017-001
E. Lazega
Abstract This paper is the text prepared for the keynote address of the EUSN 2017 conference in Mainz, Germany. A short presentation of concepts reflects in part the foundations of neo-structural sociology (NSS) and its use of social and organisational network analyses, combined with other methodologies, to better understand the roles of structure and culture in individual and collective agency. The presentation shows how NSS accounts for institutional change by focusing on the importance of combined relational infrastructures and rhetorics. Specific characteristics of institutional entrepreneurs who punch above their weight in institutionalization processes are introduced for that purpose, particularly the importance of multi-status oligarchs, status heterogeneity, high-status inconsistencies, collegial oligarchies, conflicts of interests and rhetorics of relative/false sacrifice. Two empirical examples illustrate this approach. The first case focuses on a network study of the Commercial Court of Paris, a 450-year-old judicial institution. The second case focuses on a network study of a field-configuring event (the so-called Venice Forum) lobbying for the emergence of a new European jurisdiction, the Unified Patent Court, and its attempt to create a common intellectual property regime for the continent. For sociologists, both examples involve “studying up”: they are cases of public/private joint regulation of markets bringing together these ingredients of institutionalization. The conclusion suggests future lines of research that NSS opens for the study of institutionalization, in particular using the dynamics of multi-level networks. One of the main issues raised by this approach is its contribution to the study of democratic deficits in a period of intense institutional change in Europe.
{"title":"Networks and Institutionalization: A Neo-structural Approach","authors":"E. Lazega","doi":"10.21307/CONNECTIONS-2017-001","DOIUrl":"https://doi.org/10.21307/CONNECTIONS-2017-001","url":null,"abstract":"Abstract This paper is the text prepared for the keynote address of the EUSN 2017 conference in Mainz, Germany. A short presentation of concepts reflects in part the foundations of neo-structural sociology (NSS) and its use of social and organisational network analyses, combined with other methodologies, to better understand the roles of structure and culture in individual and collective agency. The presentation shows how NSS accounts for institutional change by focusing on the importance of combined relational infrastructures and rhetorics. Specific characteristics of institutional entrepreneurs who punch above their weight in institutionalization processes are introduced for that purpose, particularly the importance of multi-status oligarchs, status heterogeneity, high-status inconsistencies, collegial oligarchies, conflicts of interests and rhetorics of relative/false sacrifice. Two empirical examples illustrate this approach. The first case focuses on a network study of the Commercial Court of Paris, a 450-year-old judicial institution. The second case focuses on a network study of a field-configuring event (the so-called Venice Forum) lobbying for the emergence of a new European jurisdiction, the Unified Patent Court, and its attempt to create a common intellectual property regime for the continent. For sociologists, both examples involve “studying up”: they are cases of public/private joint regulation of markets bringing together these ingredients of institutionalization. The conclusion suggests future lines of research that NSS opens for the study of institutionalization, in particular using the dynamics of multi-level networks. One of the main issues raised by this approach is its contribution to the study of democratic deficits in a period of intense institutional change in Europe.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"37 1","pages":"7 - 22"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46385610","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}
Abstract The article describes datasets from network exchange experiments collected at the University of South Carolina Laboratory for Sociological Research during 1989-1998. These datasets record time stamped negotiations between subjects as they seek to complete exchanges with one another.
{"title":"The South Carolina Network Exchange Datasets","authors":"J. Skvoretz","doi":"10.17266/35.2.5","DOIUrl":"https://doi.org/10.17266/35.2.5","url":null,"abstract":"Abstract The article describes datasets from network exchange experiments collected at the University of South Carolina Laboratory for Sociological Research during 1989-1998. These datasets record time stamped negotiations between subjects as they seek to complete exchanges with one another.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"36 1","pages":"58 - 61"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67582746","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}
Abstract This article surveys laboratory experiments on social exchange networks. The method of laboratory experiments is prominent in this field. The various theoretical perspectives informing the experiments are grouped into three approaches: the first, dominated by network-exchange theory, is mainly concerned with power and structure, the second discusses social-psychological approaches and emphasizes behavioral and psychological dimensions such as reciprocity, emotions and cohesion, and the third is concerned with game-theoretic experiments embedded in network structures.
{"title":"Social Exchange Networks: A Review of Experimental Studies","authors":"S. Neuhofer, Ilona Reindl, Bernhard Kittel","doi":"10.17266/35.2.3","DOIUrl":"https://doi.org/10.17266/35.2.3","url":null,"abstract":"Abstract This article surveys laboratory experiments on social exchange networks. The method of laboratory experiments is prominent in this field. The various theoretical perspectives informing the experiments are grouped into three approaches: the first, dominated by network-exchange theory, is mainly concerned with power and structure, the second discusses social-psychological approaches and emphasizes behavioral and psychological dimensions such as reciprocity, emotions and cohesion, and the third is concerned with game-theoretic experiments embedded in network structures.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"36 1","pages":"34 - 51"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67583174","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}
Abstract This paper presents three items. The first is a brief outline of structural balance oriented towards tracking the amount of balance (or imbalance) over time in signed networks. Often, the distribution of specific substructures within broader networks has great interest value. The second item is a brief outline of a procedure in Pajek for identifying fragments in networks. Identifying fragments (or patterns or motifs) in networks has general utility for social network analysis. The third item is the application of the notion of fragments to counting signed triples and signed 3-cycles in signed networks. Commands in Pajek are provided together with the use of Pajek project files for identifying fragments in general and signed fragments in particular. Our hope is that this will make an already available technique more widely recognized and used. Determining fragments need not be confined to signed networks although this was the primary application considered here.
{"title":"Identifying Fragments in Networks for Structural Balance and Tracking the Levels of Balance Over Time","authors":"P. Doreian, Andrej Mrvar","doi":"10.17266/35.2.1","DOIUrl":"https://doi.org/10.17266/35.2.1","url":null,"abstract":"Abstract This paper presents three items. The first is a brief outline of structural balance oriented towards tracking the amount of balance (or imbalance) over time in signed networks. Often, the distribution of specific substructures within broader networks has great interest value. The second item is a brief outline of a procedure in Pajek for identifying fragments in networks. Identifying fragments (or patterns or motifs) in networks has general utility for social network analysis. The third item is the application of the notion of fragments to counting signed triples and signed 3-cycles in signed networks. Commands in Pajek are provided together with the use of Pajek project files for identifying fragments in general and signed fragments in particular. Our hope is that this will make an already available technique more widely recognized and used. Determining fragments need not be confined to signed networks although this was the primary application considered here.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"36 1","pages":"6 - 18"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67583031","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}
Pub Date : 2016-01-01DOI: 10.21307/connections-2016-020
C. Ansell, Renata Bichir, Shi Zhou
Abstract Departing from Roberto Michels’s classic analysis of oligarchy, we provide a structural analysis of the concept based on social network analysis. We define oligarchy as a social network that exhibits three structural properties: tight interconnections among a small group of prominent actors who form an “inner circle”; the organization of other actors in the network through the intermediation of this inner circle; and weak direct connections among the actors outside the inner circle. We treat oligarchy as a global property of social networks and offer an approach for measuring the oligarchical tendencies of any social network. Our main contribution is to operationalize this idea using a “rich club” approach. We demonstrate the efficacy of this approach by analyzing and comparing several urban networks: Sao Paulo urban infrastructure networks and Los Angeles and Chicago transportation policy networks.
{"title":"Who Says Networks, Says Oligarchy? Oligarchies as “Rich Club” Networks","authors":"C. Ansell, Renata Bichir, Shi Zhou","doi":"10.21307/connections-2016-020","DOIUrl":"https://doi.org/10.21307/connections-2016-020","url":null,"abstract":"Abstract Departing from Roberto Michels’s classic analysis of oligarchy, we provide a structural analysis of the concept based on social network analysis. We define oligarchy as a social network that exhibits three structural properties: tight interconnections among a small group of prominent actors who form an “inner circle”; the organization of other actors in the network through the intermediation of this inner circle; and weak direct connections among the actors outside the inner circle. We treat oligarchy as a global property of social networks and offer an approach for measuring the oligarchical tendencies of any social network. Our main contribution is to operationalize this idea using a “rich club” approach. We demonstrate the efficacy of this approach by analyzing and comparing several urban networks: Sao Paulo urban infrastructure networks and Los Angeles and Chicago transportation policy networks.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"189 1","pages":"20 - 32"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67974517","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}
Pub Date : 2016-01-01DOI: 10.21307/connections-2016-062
S. Gesell, Eric A. Tesdahl
Abstract The Madre Sana data set was compiled as a part of a community-engaged health promotion research study. The data set includes 150 actor variables plus multiplex edges between study participants (N=116 pregnant women) at two time points.
Madre Sana数据集是一项社区参与的健康促进研究的一部分。数据集包括150个行动者变量和两个时间点研究参与者(N=116名孕妇)之间的多重边缘。
{"title":"The “Madre Sana” Data Set","authors":"S. Gesell, Eric A. Tesdahl","doi":"10.21307/connections-2016-062","DOIUrl":"https://doi.org/10.21307/connections-2016-062","url":null,"abstract":"Abstract The Madre Sana data set was compiled as a part of a community-engaged health promotion research study. The data set includes 150 actor variables plus multiplex edges between study participants (N=116 pregnant women) at two time points.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"36 1","pages":"62 - 65"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67974534","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}
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