Partitioning signed social networks

Structural balance theory has proven useful for delineating the blockmodel structure of signed social networks. Even so, most of the observed signed networks are not perfectly balanced. One possibility for this is that in examining the dynamics underlying the generation of signed social networks, insufficient attention has been given to other processes and features of signed networks. These include: actors who have positive ties to pairs of actors linked by a negative relation or who belong to two mutually hostile subgroups; some actors that are viewed positively across the network despite the presence of negative ties and subsets of actors with negative ties towards each other. We suggest that instead viewing these situations as violations of structural balance, they can be seen as belonging to other relevant processes we call mediation, differential popularity and internal subgroup hostility. Formalizing these ideas leads to the relaxed structural balance blockmodel as a proper generalization of structural balance blockmodels. Some formal properties concerning the relation between these two models are presented along with the properties of the fitting method proposed for the new blockmodel type. The new method is applied to four empirical data sets where improved fits with more nuanced interpretations are obtained.     

A multiple indicator approach to blockmodeling signed networks

Regardless of whether the focus is on algebraic structures, elaborating role structures or the simple delineation of concrete social structures, generalized blockmodeling faces a pair of vulnerabilities. One is sensitivity to poor quality of the relational data and the other is a risk of over fitting blockmodels to the details of specific networks. Over fitting blockmodels can lead to multiple equally well fitting partitions where choices cannot be made between them on a principled basis. This paper presents a method of tackling these problems by viewing (when possible) observed social relations as multiple indicators of an underlying affect dimension. Quadratic assignment methods using matching coefficients, product moment correlations and Goodman and Kruskal's gamma are used to assess the appropriateness of using the sum of observed relations as input for applying generalized blockmodeling. Data for four groups are used to show the value of this approach within which multiple equally well fitting blockmodels for single relations are replaced by unique (or near-unique) partitions of the summed data. This strategy is located also within a broader problem of blockmodeling three-dimensional networks data and suggestions are made for future work.     

Clustering in weighted networks

In recent years, researchers have investigated a growing number of weighted networks where ties are differentiated according to their strength or capacity. Yet, most network measures do not take weights into consideration, and thus do not fully capture the richness of the information contained in the data. In this paper, we focus on a measure originally defined for unweighted networks: the global clustering coefficient. We propose a generalization of this coefficient that retains the information encoded in the weights of ties. We then undertake a comparative assessment by applying the standard and generalized coefficients to a number of network datasets.     

Multiple partitioning of multiplex signed networks: Application to European parliament votes

For more than a decade, graphs have been used to model the voting behavior taking place in parliaments. However, the methods described in the literature suffer from several limitations. The two main ones are that 1) they rely on some temporal integration of the raw data, which causes some information loss; and/or 2) they identify groups of antagonistic voters, but not the context associated with their occurrence. In this article, we propose a novel method taking advantage of multiplex signed graphs to solve both these issues. It consists in first partitioning separately each layer, before grouping these partitions by similarity. We show the interest of our approach by applying it to a European Parliament dataset.     

Node centrality in weighted networks: Generalizing degree and shortest paths

Ties often have a strength naturally associated with them that differentiate them from each other. Tie strength has been operationalized as weights. A few network measures have been proposed for weighted networks, including three common measures of node centrality: degree, closeness, and betweenness. However, these generalizations have solely focused on tie weights, and not on the number of ties, which was the central component of the original measures. This paper proposes generalizations that combine both these aspects. We illustrate the benefits of this approach by applying one of them to Freeman’s EIES dataset.     

A new look at clustering coefficients with generalization to weighted and multi-faction networks

In this paper we propose a new method for studying local and global clustering in networks employing random walk pairs. The method is intuitive and directly generalizes standard local and global clustering coefficients to weighted networks and networks containing nodes of multiple types. In the case of two-mode networks the values obtained for commonly considered social networks are in sharp contrast to those obtained, for instance, by the method of Opsahl (2013), and provide a different viewpoint for clustering. The approach is also applicable in questions related to the general study of segregation and homophily. Applications to existent data sets are considered.     

上海产业结构调整及产业转移趋势研究

目前上海已呈现后工业化时期产业发展的阶段性特征,进入发展转型的重要时期,产业结构调整与优化升级也面临战略性转变,并呈现出特有的方向与重点。同时,产业对外转移已成为上海产业结构升级的重要途径,其趋势体现于规模扩张型、功能整合型和战略扩张型产业转移,并集中体现在一些重点领域。     

Approaches to blockmodeling dynamic networks: A Monte Carlo simulation study

Blockmodeling refers to a variety of statistical methods for reducing and simplifying large and complex networks. While methods for blockmodeling networks observed at one time point are well established, it is only recently that researchers have proposed several methods for analysing dynamic networks (i.e., networks observed at multiple time points). The considered approaches are based on k-means or stochastic blockmodeling, with different ways being used to model time dependency among time points. Their novelty means they have yet to be extensively compared and evaluated and the paper therefore aims to compare and evaluate them using Monte Carlo simulations. Different network characteristics are considered, including whether tie formation is random or governed by local network mechanisms. The results show the Dynamic Stochastic Blockmodel (Matias and Miele 2017) performs best if the blockmodel does not change; otherwise, the Stochastic Blockmodel for Multipartite Networks (Bar-Hen et al. 2020) does.     

Exponential random graph models of preschool affiliative networks

Exponential random graph models were used to assess the relevance of reciprocity, popularity, transitivity controlling for effects based upon ego and alter sex, and sex homophily, on the formation of affiliative ties in 19 Portuguese preschool peer groups. The number of times two children were recorded as nearest neighbors in focal samples was used as an indicator of the relationship's strength. Independent parameter estimates of the different models (one for each group) were summarized, separately for the three age groups (“3-year-olds”, “4-year-olds” and “5-year-olds”) using a multi-level approach to meta-analysis. Results showed that affiliative ties between children were sex segregated, highly reciprocal, more likely to be directed to a restricted number of children and with a tendency to create transitive triads. The structural processes underlying the formation of affiliative ties were quite stable between classrooms.     

A deviational approach to blockmodeling of valued networks

This article proposes a novel approach to blockmodeling of valued (one-mode) networks where the identification of (binary) block patterns in the valued relations differ from existing approaches. Rather than looking at the absolute values of relations, or examining valued ties on a per-actor basis (cf. Nordlund, 2007), the approach identifies prominent (binary) ties on the basis of deviations from expected values. By comparing the distribution of each actor's valued relations to its alters with the macro-level distributions of total in- and outdegrees, prominent (1) and non-prominent (0) ties are determined both on a per-actor-to-actor and a per-actor-from-actor basis. This allows for a direct interpretation of the underlying functional anatomy of a non-dichotomized valued network using the standard set of ideal blocks as found in generalized blockmodeling of binary networks.In addition to its applicability for direct blockmodeling, the article also suggests a novel indirect measure of deviational structural equivalence on the basis of such deviations from expected values.Exemplified with the note-sharing data in Žiberna (2007a), citations among social work journals (Baker, 1992), and total commodity trade among EU/EFTA countries as of 2010, both the direct and indirect approach produce results that are more sensitive to variations at the dyadic level than existing approaches. This is particularly evident in the case of the EU/EFTA trade network, where the indirect approach yields partitions and blockmodels in support of theories of regional trade, despite the significantly skewed valued degree distribution of the dataset.     

Maternal involvement and adolescents' psychological and academic adjustment after COVID-19 return-to-school: A one-year longitudinal study

The current study examined the idea that maternal involvement during the COVID-19 school closure period could contribute to the development of adolescents' daily routines, which could ultimately associate with their psychological and academic adjustment after return-to-school. Data were collected from 520 Chinese adolescents (M_age = 12.75 years, SD_age = 1.48 years) and their mothers during the COVID-19 school closure and one year after return-to-school. Results indicated that maternal involvement in both education and leisure activities predicted more consistent adolescents' daily routines during the school closure period and then contributed to their more consistent daily routines after return-to-school, resulting in less psychological maladjustment and better academic outcomes. Findings highlight the importance of maternal involvement and daily routines in the context of COVID-19.     

Parameter estimation procedures for exponential-family random graph models on count-valued networks: A comparative simulation study

The exponential-family random graph models (ERGMs) have emerged as an important framework for modeling social networks for a wide variety of relational types. ERGMs for valued networks are less well-developed than their unvalued counterparts, and pose particular computational challenges. Network data with edge values on the non-negative integers (count-valued networks) is an important such case, with examples ranging from the magnitude of migration and trade flows between places to the frequency of interactions and encounters between individuals. Here, we propose an efficient parallelizable subsampled maximum pseudo-likelihood estimation (MPLE) scheme for count-valued ERGMs, and compare its performance with existing Contrastive Divergence (CD) and Monte Carlo Maximum Likelihood Estimation (MCMLE) approaches via a simulation study based on migration flow networks in two U.S. states. Our results suggest that edge value variance is a key factor in method performance, while network size mainly influences their relative merits in computational time. For small-variance networks, all methods perform well in point estimations while CD greatly overestimates uncertainties, and MPLE underestimates them for dependence terms; all methods have fast estimation for small networks, but CD and subsampled multi-core MPLE provides speed advantages as network size increases. For large-variance networks, both MPLE and MCMLE offer high-quality estimates of coefficients and their uncertainty, but MPLE is significantly faster than MCMLE; MPLE is also a better seeding method for MCMLE than CD, as the latter makes MCMLE more prone to convergence failure. The study suggests that MCMLE and MPLE should be the default approach to estimate ERGMs for small-variance and large-variance valued networks, respectively. We also offer further suggestions regarding choice of computational method for valued ERGMs based on data structure, available computational resources and analytical goals.     

Extended structures of mediation: Re-examining brokerage in dynamic networks

In this paper we revisit the concept of brokerage in social networks. We elaborate on the concept of brokerage as a process, identifying three distinct classes of brokerage behavior. Based on this process model, we develop a framework for measuring brokerage opportunities in dynamic relational data. Using data on emergent inter-organizational collaborations, we employ the dynamic brokerage framework to examine the relationship between organizational attributes and coordination in the evolving network. Comparing the findings of our process-based definition with traditional, static approaches, we identify important dimensions of organizational action that would be missed by the latter approach.     

Teachers as risk and resource factors in minority students’ school adjustment: an integrative review of qualitative research on acculturation

ABSTRACT

The present study is embedded in the contextual approach of acculturation research. It examines teachers’ attitudes and behaviors, teachers’ expectations as well as their teaching practices as risk and resource factors in minority students’ school adjustment. The study applies the method of content analysis and uses a deductively developed and inductively enriched system of categories. The results of the study provide an integrative review of qualitative research on the role teachers play in the acculturation process of minority youth and their adjustment outcomes in the school context.     

Understanding labour processes in global production networks: a case study of the football industry in Pakistan

ABSTRACT

Over the last few decades, the changing nature of global production and distribution processes has raised a number of critical questions regarding work and employment relations. Presenting a qualitative case study of the football industry in Pakistan as an example of the general mechanism of the social relations of re/production in a global system of industrial organization, this research highlights how and under what conditions informal workers are embedded in extended global production networks. By drawing on the integrated conceptual framework of the global production network (GPN) and labour process theory (LPT), this research sheds light on the working conditions and living realities of informal workers. A potential contribution of this paper is to extend the horizon of production network theory by analysing the work and employment conditions of informal workers, which are absent in existing discussions of these conceptual frameworks.     

Hope and adjustment to college in the context of collective trauma

Objectives: To understand predictors of first-year college adjustment in the context of collective trauma, including a school shooting. Participants: Two consecutive years of entering freshman (Year 1 (Y1) N = 169, Year 2 (Y2) N = 94) were surveyed over time: Y1 in October 2012 and March 2013, and Y2 in October 2013 and August 2014. During Y2, several collective traumas occurred, impacting the campus community and providing a stark comparison. Methods: Online surveys measuring predictors at the start of each year and adjustment at the end of each year. Results: Hope was the only significant predictor of adjustment when included in a regression model with depression symptoms in Y1, and with depression symptoms, baseline posttraumatic stress symptoms, and traumatic exposure in Y2. Conclusions: Fostering hope may promote college adjustment, regardless of collective traumas that might occur on campus.     

Are larger cities more central in urban networks: A meta-analysis

As cities develop more and longer-range external relations, some have challenged the long-standing notion that population size indicates a city's power in its urban system. But are population size and network centrality really independent properties in practice, or do larger cities tend to be more central in urban networks? To answer this question, we conducted a systematic literature search and meta-analysed 41 reported correlations between city size and degree centrality. The results show that population size and degree centrality are significantly and positively correlated for cities across various urban systems (r = 0.77), but the correlation varies by network scale and type. The size-centrality association is weaker for global economic and transportation networks (r = 0.43), and stronger for non-global social and communication networks (r = 0.91). This clarifies seemingly contradictory predictions in the literature regarding the association betweensize and centrality for cities.     

Communities in criminal networks: A case study

Criminal organizations tend to be clustered to reduce risks of detection and information leaks. Yet, the literature exploring the relevance of subgroups for their internal structure is so far very limited. The paper applies methods of community analysis to explore the structure of a criminal network representing the individuals’ co-participation in meetings. It draws from a case study on a large law enforcement operation (“Operazione Infinito”) tackling the ‘Ndrangheta, a mafia organization from Calabria, a southern Italian region. The results show that the network is indeed clustered and that communities are associated, in a non-trivial way, with the internal organization of the ‘Ndrangheta into different “locali” (similar to mafia families). Furthermore, the results of community analysis can improve the prediction of the “locale” membership of the criminals (up to two thirds of any random sample of nodes) and the leadership roles (above 90% precision in classifying nodes as either bosses or non-bosses). The implications of these findings on the interpretation of the structure and functioning of the criminal network are discussed.     

A comparison of email networks and off-line social networks: A study of a medium-sized bank

Recently there has been a surge in the availability of online data concerning the connections between people, and these online data are now widely used to map the social structure of communities. There has been little research, however, on how these new types of relational data correspond to classical measures of social networks. To fill this gap, we contrast the structure of an email network with the underlying friendship, communication, and advice seeking networks. Our study is an explorative case study of a bank, and our data contains emails among employees and a survey of the ego networks of the employees. Through calculating correlations with QAP standard errors and estimating exponential random graph (ERG) models, we find that although the email network is related to the survey-based social networks, email networks are also significantly different: while off-line social networks are strongly shaped by gender, tenure, and hierarchical boundaries, the role of these boundaries are much weaker in the email network.     

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.