Fuzzy duocentric community detection model in social networks

The main goal of this paper is to present a clustering model to identify duocentric communities in the complex networks. A duocentric community is built around two central nodes which are as close as possible to other nodes, while the central nodes are connected enough to each other to shape the center of the community. To detect such communities, we develop a new objective function based clustering model. The network's nodes are assigned to the duocentric communities by the type-2 fuzzy numbers which indicate the degrees of belonging to the communities by upper and lower membership values. Generated interval type-2 fuzzy membership values by our proposed model are able to determine how much each node belongs to the both central nodes and how it is shared among communities. Also, the compatible verification index with the proposed model is introduced to evaluate and compare the results of the proposed model with the existing approach in the literature. Finally, the performance of the proposed algorithm is validated by detecting duocentric communities in three artificial networks and two real social networks.     

Betweenness computation in the single graph representation of hypergraphs

Many real-world social networks are hypergraphs because they either explicitly support membership in groups or implicitly include communities. We present the HyperBC algorithm that exactly computes betweenness centrality (or BC) in hypergraphs. The forward phase of HyperBC and the backpropagation phase are specifically tailored for BC computation on hypergraphs. In addition, we present an efficient method for pruning networks through the notion of “non-bridging” vertices. We experimentally evaluate our algorithm on a variety of real and artificial networks and show that it significantly speeds up the computation of BC on both real and artificial hypergraphs, while at the same time, being very memory efficient.     

Exponential random graph models for multilevel networks

Modern multilevel analysis, whereby outcomes of individuals within groups take into account group membership, has been accompanied by impressive theoretical development (e.g. Kozlowski and Klein, 2000) and sophisticated methodology (e.g. Snijders and Bosker, 2012). But typically the approach assumes that links between groups are non-existent, and interdependence among the individuals derives solely from common group membership. It is not plausible that such groups have no internal structure nor they have no links between each other. Networks provide a more complex representation of interdependence. Drawing on a small but crucial body of existing work, we present a general formulation of a multilevel network structure. We extend exponential random graph models (ERGMs) to multilevel networks, and investigate the properties of the proposed models using simulations which show that even very simple meso effects can create structure at one or both levels. We use an empirical example of a collaboration network about French cancer research elites and their affiliations (0125 and 0120) to demonstrate that a full understanding of the network structure requires the cross-level parameters. We see these as the first steps in a full elaboration for general multilevel network analysis using ERGMs.     

Wann helfen wir einander? Befunde zur Bedeutung der Gruppenzugehörigkeit

How are prosocial behavior and group membership connected? Based on the theory of social identity social psychologists found: People help each other when they feel they belong to the same group (In-Group helping). Out-Group helping occours too, but for different, if you wish: selfish, reasons. To the extent one can manipulate the feeling of belonging it is possible to change the willingsness to help others.     

The impact of social networks on the occupational status of migrants

This paper postulates that there is a continuous exchange of information and knowledge between those who share the common bond of having migrated to the US. The individual components of this information exchange constitute social networks. The 2 hypotheses tested are 1) immediate social networks and people known in the US facilitate the flow of information both to new migrants and between established migrants, thus promoting upward social mobility; and 2) access to broader network ties, organization membership, extra-ethnic friendships, and familiarity with established institutions smooths the transition process, resulting in increased social position. The data used comes from a study conducted in 1982-1983 in 4 Mexican sending communities (2 rural, 2 urban), for a total of 440 migrants. Results show that migrants in every socioeconomic bracket reported access to some or all social network characteristics. There was contact with either a family member or acquaintances from the migrants' town of origin. Over 50% of migrants reported knowing many fellow townspeople. Twice as many migrants belong to a sports club as to a social or religious organization. Very few rural migrants report knowing no townspeople, while 32% of urban migrants claim no knowledge of fellow migrants from their town of origin. Urban origin migrants report more contacts with those of other ethnicity than rural migrants. Those employed in agriculture are least acquainted with social information and contacts, while those in skilled and service sectors are well acquainted with them. The results of fact and analysis show that 1) access to personal US networks results in an average 4.4 point advantage in occupational prestige scores over no access, and 2) utilizing institutional US networks combined with any cumulative US experience gives a migrant a 5 point advantage over a fellow migrant with identical experience level but no institutional network contacts. This is also true for institutional Mexican networks. Thus success or failure in migrating is partly due to migrants' societal infrastructure and the fact that available information and social networks are accessed and utilized differently by different migrants.     

The Joiners: Active Voluntary Association Membership in Twenty African Countries

To what extent do sub-Saharan Africans actively participate in voluntary groups, and how does development aid influence this involvement? This paper presents a baseline assessment of membership predictors for secular and religious voluntary groups across 20 sub-Saharan African countries. Using Afrobarometer survey data, I adopt an asset-based resources approach in which development aid is seen to mobilize local resources and increase incentives to join voluntary groups. The theory is tested with multilevel, cross-national logistic regression models, including both individual-level and country-level variables to predict active membership. At the individual level, I find that membership in voluntary groups is most likely among those who are well educated, rural-dwelling, male, black, and middle aged—a reflection of social advantage. However, poorer Africans are also likely to join such groups, as are those with a strong religious affinity. At the national level, development aid is positively tied to voluntary group membership, but democratic and economic progresses have little to no consequence on this behavior.     

Cluster analysis of multiplex networks: Defining composite network measures

Social relations are multiplex by nature: actors in a group are tied together by various types of relationships. To understand and explain group processes it is, therefore, important to study multiple social networks simultaneously in a given group. However, with multiplexity the complexity of data also increases. Although some multivariate network methods (e.g. Exponential Random Graph Models, Stochastic Actor-oriented Models) allow to jointly analyze multiple networks, modeling becomes complicated when it focuses on more than a few (2–4) network dimensions. In such cases, dimension reduction methods are called for to obtain a manageable set of variables. Drawing on existing statistical methods and measures, we propose a procedure to reduce the dimensions of multiplex network data measured in multiple groups. We achieve this by clustering the networks using their pairwise similarities, and constructing composite network measures as combinations of the networks in each resulting cluster. The procedure is demonstrated on a dataset of 21 interpersonal network dimensions in 18 Hungarian high-school classrooms. The results indicate that the network items organize into three well-interpretable clusters: positive, negative, and social role attributions. We show that the composite networks defined on these three relationship groups overlap but do not fully coincide with the network measures most often used in adolescent research, such as friendship and dislike.     

What's in the “old boys” network? Accessing social capital in gendered and racialized networks

Network processes have long been implicated in the reproduction of labor market inequality, but it remains unclear whether white male networks provide more social capital resources than female and minority networks. Analysis of nationally representative survey data reveals that people in white male networks receive twice as many job leads as people in female/minority networks. White male networks are also comprised of higher status connections than female/minority networks. The information and status benefits of membership in these old boy networks accrue to all respondents and not just white men. Furthermore, gender homophilous contacts offer greater job finding assistance than other contacts. The results specify how social capital flows through gendered and racialized networks.     

The Company You Keep: Adolescent Mobility and Peer Behavior*

Residential mobility has been linked to a variety of problematic behaviors during adolescence, but the reasons for this association are not well understood. This analysis examines the relationship between adolescent residential mobility and the academic and deviant behaviors of members of adolescents’ friendship networks. Using data from approximately 12,000 respondents in the National Longitudinal Study of Adolescent Health (Add Health), we find that residentially mobile adolescents belong to school‐based friendship networks whose members exhibit weaker academic performance and expectations, less school engagement, and higher rates of deviance than do members of the friendship networks of nonmobile adolescents, even after controlling for adolescents’ own academic and deviant behaviors. These differences in the behavioral composition of adolescent friendship networks are not ephemeral, but appear to persist for several years. Moreover, these differences are equally pronounced among older and younger adolescents and among girls and boys. We also find that parental socioeconomic status is positively associated with adolescents’ involvement in high‐achieving and prosocial friendship networks. Directions for future research exploring the impact of residential and school mobility on adolescent development and functioning are discussed.     

VOLUNTARY ASSOCIATION MEMBERSHIP AMONG BLACK AMERICANS: THE POST-CIVIL RIGHTS ERA

This article discusses voluntary association membership among black Americans in the post-civil rights era. Previous empirical studies comparing black to white social participation are critically assessed. A model composed of social class and intragroup regionality variables is offered to explain membership patterns as a manifestation of vertical and horizontal differentiation in the black American social structure. The analysis of empirical data collected by telephone interviews (N= 321) in a southwest Chicago community revealed that education most accurately predicts membership across associations but that occupation and regionality are also important variables. The analysis showed that members of church groups and civil rights groups are likely to be women from the rural South with at least 12 years of education. These findings suggest that explanations of black American life in the post-civil rights era may wish to consider vertical as well as horizontal differentiation.     

Americans’ Perceptions of Commitment to Strong Families Across Race and Ethnic Groups

Theories of prejudice examining perceptual differences of in-groups and out-groups, such as social dominance and group threat perspectives, have found them to be important in generating hegemonic identity formations. This study uses data from the General Social Surveys to explore how these hegemonic group perceptions influence attitudes toward stronger families using race and ethnic groups as proxy measures where Whites are considered the in-group. A modified ecosystemic-interactionist approach along with elements from group threat and social dominance theories as support was used to investigate the underlying premise that group membership influenced respondents’ perceptions of their own family vis á vis others. Overall, the study found support for the underlying premise that membership influenced group perceptions, group knowledge, and group beliefs about commitment to strong families. Additionally, these findings suggest that groups develop most of their beliefs and subsequently their attitudes from limited interaction with those they perceived to be in the out-groups. Other social factors, including media portrayals, geographic propinquity, and education, also play a role in group perceptions, group knowledge, and group beliefs about commitment toward strong families. They support the social dominance and group threat theories that give rise to hegemonic structure within the United States.     

Latent classes and group membership

A technique for analyzing group membership data, such as interlocking directorates, based on the assumption of latent classes of individuals, is described and illustrated with two data sets. The technique partitions individuals and/or groups into homogeneous sets and can be used to create measures of structural centrality for groups and for individuals.     

Estimating the effects of network covariates on subgroup insularity with a hierarchical mixed membership stochastic blockmodel

Social networks describe the relationships and interactions among a group of individuals. In many peer relationships, individuals tend to associate more often with some members than others, forming subgroups or clusters. Subgroup structure varies across networks; subgroups may be insular, appearing distinct and isolated from one another, or subgroups may be so integrated that subgroup structure is not visually apparent, and there are numerous ways of quantifying these types of structures. We propose a new model that relates the amount of subgroup integration to network attributes, building on the mixed membership stochastic blockmodel (Airoldi et al., 2008) and subsequent work by Sweet and Zheng (2017) and Sweet et al. (2014). We explore some of the operating characteristics of this model with simulated data and apply this model to determine the relationship between teachers’ instructional practices and their classrooms’ peer network subgroup structure.     

FIRM HETEROGENEITY AND THE PATTERN OF R&D COLLABORATIONS

We consider an oligopoly setting in which firms form pairwise collaborative links in research and development with other firms. Each collaboration generates a value that depends on the identity of the firms that collaborate. First, we provide properties satisfied by pairwise equilibrium networks and efficient networks. Second, we use these properties in two types of situation: (1) there are two groups of firms, and the value of a collaboration is higher when firms belong to the same group; (2) some firms have more innovative capabilities than others. These two situations provide clear insights about how firms' heterogeneity affects both equilibrium and efficient networks. We also show that the most valuable collaborative links do not always appear in equilibrium, and a public policy that increases the value of the most valuable links may lead to a loss of social welfare. (JEL C70, L13, L20)     

The multipolar regionalization of cities in multinational firms' networks

This article identifies the most cohesive multi‐polar regions of the network of world cities, which differ from the unipolar centre–periphery model largely created by the high weight of central city connections. We use a community detection algorithm that outlines the high densities of city networks (in relative weights). Various patterns of industries and services, which are divided into two skill levels, are identified. We use a global database of the network of 1.2 million direct and indirect ownership links between the 800,000 subsidiaries of the top 3000 multinational groups in 2013, allowing us to build four comparable networks of 503 metropolitan areas. Comparing the obtained partitions with continental, regional and economic benchmarks, classes of cities partially correspond to free trade zones (FTZs) but exhibit interesting cross‐continental patterns. A few cities, changing their classes according to the activities, are discussed in the light of the multinational firms' strategies.     

Social Sources of Community Solidarity in U.S. Congregations

Community solidarity, or a collective sense of belonging, plays a vital role in the health and survival of many organizations. Consequently, identifying the elements that contribute to a strong sense of solidarity within communities has long been a topic of inquiry for scholars. In this study, we draw upon prior theorizing to develop and test four hypotheses regarding the organizational characteristics associated with community solidarity in religious congregations. Multivariate models are estimated using national data on religious congregations from the 2001 U.S. Congregational Life Survey (n?=?357). Organizations with greater community solidarity tend to feature higher levels of social capital (operationalized with measures of friendship networks and participation in organizational activities), higher levels of official membership within the organization, and inspiring congregational leadership. Higher levels of commitment are associated with greater community solidarity, but the effect is wholly mediated by social capital. Congregations that engender higher levels of community solidarity share certain organizational features, including higher levels of social capital, higher rates of membership, and inspiring leaders.     

The Emergence and Stability of Groups in Social Networks

An important puzzle in social network research is to explain how macro-level structures emerge from micro-level network processes. Explaining the emergence and stability of structural groups in social networks is particularly difficult for two reasons. First, because groups are characterized both by high connectedness within (group cohesion) and lack of connectedness between them (group boundaries). Second, because a large number of theoretical micro-level network processes contribute to their emergence. We argue that traditional social network theories that are concerned with the evolution of positive relations (forces of attraction) are not sufficient to explain the emergence of groups because they lack mechanisms explaining the emergence of group boundaries. Models that additionally account for the evolution of negative ties (forces of repulsion) may be better suited to explain the emergence and stability of groups. We build a theoretical model and illustrate its usefulness by fitting stochastic actor-oriented models (SAOMs) to empirical data of co-evolving networks of friendship and dislike among 479 secondary-school students. The SAOMs include a number of newly developed effects expressing the co-evolution between positive and negative ties. We then simulate networks from the estimated models to explore the micro-macro link. We find that a model that considers forces of attraction and repulsion simultaneously is better at explaining groups in social networks. In the long run, however, the empirically informed simulations generate networks that are too stylized to be realistic, raising further questions about model degeneracy and time heterogeneity of group processes.     

Analyzing the Impact of Bonding and Bridging Social Capital on Economic Well-Being: Results from Canada's General Social Survey

In this study we analyze nationally representative data from Canada's General Social Survey to investigate how various indicators of bonding and bridging social capital are associated with economic well-being and how the magnitude of their associations compare with each other. Our findings suggest that several dimensions of bonding social capital, including knowing neighbors well enough to ask favors of them and providing assistance to others, are positively associated with economic well-being. The study's indicators of bridging social capital were also linked to increases in the participants’ economic well-being. When comparing the associations of bonding and bridging social capital we ascertained that bridging social capital in the form of group membership, including Internet group membership and participation in groups, had a more robust association than any of the indicators of bonding social capital. We consider the implications of the study's findings in light of a technologically-advanced yet volatile economy.     

The Exclusiveness of the Political Field: Networks and political mobilization

This paper applies social network analysis to three case study social movement organizations based in the north of England: a local Labour Party branch, an environmental group, and a conservation group. Using a postal survey of members, we chart the extent of ties between members of these three groups, indicating how each group has its own internal social dynamics and characteristics that are related to the nature of the movement organizations themselves. We explore how the network structures interrelate with the socio-demographic structure of the membership of the three organizations, and we show there are important differences in the way that core members of the three organizations are recruited compared to those who are either peripheral or isolated. Our paper is the first to analyse the networks of whole populations of case study organizations in the UK, and can therefore be read as developing the potential of social network analysis for case study research and for understanding social movements. Analytically we argue it is important to distinguish two different types of ways that networks are important. They can be seen as offering resources for mobilization, or they can be seen as providing a means of integrating particular types of individuals into organizations. It is this latter sense that offers a more fundamental role for network analysis, and we argue that it offers an important way of developing insights from resource mobilization theory by relating them to Bourdieu's provocative arguments regarding the exclusiveness of the political field.     

Senior grassroots organizations in India

Organizations for the elderly at the grassroots level began to function in India from the third decade of the 20th century. They belong to two major types: Pensioners' Associations (PAs) and Senior Citizens' Associations (SCAs). PAs claim a membership of approximately 13 million and are structured and vertically organized. The SCAs are loosely organized and most are unitary. They have together a membership of less than 5 million. Programs of PAs are mainly confined to members; those of SCAs are wider but mainly urban-biased and middle-class oriented. Neither of them is powerful in lobbying. Of late, Government of India's (GOI) policy on the elderly (NPOP, 2000) has given a fillip to the SCAs which are gathering strength, and some, at least, are vying for availing GOI program benefits.