Dynamic exchange networks

Considering the theoretical and empirical untenability of static exchange networks, researchers have asked how exchange outcomes change when links are added or deleted. The present paper assesses the validity of seemingly sensible propositions concerning the effects of adding and deleting a link on (i) the payoffs of the actors in the link, (ii) the payoffs of actors in neighboring links and (iii) the variance of payoffs in the exchange network. The propositions were examined by applying expected value theory (EVT) to all 13,597 networks up to size 8. All propositions were falsified. Some falsifications of propositions could be attributed to EVTs prediction that actors use sub-optimal exchange relations. Since other well-known theories of exchange, like power-dependence theory and network exchange theory, also predict that actors use sub-optimal relations, these results are robust to selection of the theory of exchange.     

Social context and network formation: An experimental study

Recently, there has been increasing interest in determining which social network structures emerge as a consequence of the conscious actions of actors. Motivated by the belief that “networks matter” in reaching personal objectives, it is a natural assumption that actors try to optimize their network position. Starting from the notion that an optimal network position depends on the social context, we examine how actors change their networks to reach better positions in various contexts. Distinguishing between three social contexts (a neutral context, a context in which closed triads are costly, and a context in which closed triads are beneficial), theoretical results predict that emerging networks are contingent on the incentives that are present in these contexts. Experiments are used to test whether networks that are theoretically predicted to be stable are also stable experimentally. We find that emerging networks correspond to a large extent with the predicted networks. Consequently, they are contingent on the incentives present in various social contexts. In addition, we find that subjects tend to form specific stable networks with a higher probability than predicted, namely, efficient networks and networks in which everyone is equally well off.     

Introduction to stochastic actor-based models for network dynamics

Stochastic actor-based models are models for network dynamics that can represent a wide variety of influences on network change, and allow to estimate parameters expressing such influences, and test corresponding hypotheses. The nodes in the network represent social actors, and the collection of ties represents a social relation. The assumptions posit that the network evolves as a stochastic process ‘driven by the actors’, i.e., the model lends itself especially for representing theories about how actors change their outgoing ties. The probabilities of tie changes are in part endogenously determined, i.e., as a function of the current network structure itself, and in part exogenously, as a function of characteristics of the nodes (‘actor covariates’) and of characteristics of pairs of nodes (‘dyadic covariates’). In an extended form, stochastic actor-based models can be used to analyze longitudinal data on social networks jointly with changing attributes of the actors: dynamics of networks and behavior.     

Centrality and power revisited: actor success in group decision making

For many years, network analysts viewed positional centrality as a source of social power. More recently, laboratory studies of exchange networks have called the centrality–power link into question: under zero-sum exchange conditions, the ability of certain actors to directly exploit others has been found to account for power independent of actors' centrality. But most observers believe that in non-zero-sum communication networks, centrality should positively affect power. In this study we examine the effect of centrality on power in a communication network involving group voting on political issues. Using a model in which actors' votes are determined by the strength of their initial positions and the social pressures to which they are subjected, we conduct computer simulations to examine the extent to which actors in various network positions achieve favorable political outcomes. Our findings indicate that the link between centrality and power is highly contingent on the structure of the network. In networks with a central actor and an odd number of subgroups, central actors fail to dominate. In fact, in these networks, when peripheral actors are able to directly influence one another, the central actor becomes the least powerful in the network. In networks with a central actor and an even number of subgroups, however, the central actor dominates even in situations with connected peripherals. The highly contingent effect of centrality on power accords with the findings of exchange theorists who have studied power under zero-sum conditions. This raises questions about the nature of the distinction between communication and exchange networks.     

Analysing networks of networks

We consider data with multiple observations or reports on a network in the case when these networks themselves are connected through some form of network ties. We could take the example of a cognitive social structure where there is another type of tie connecting the actors that provide the reports; or the study of interpersonal spillover effects from one cultural domain to another facilitated by the social ties. Another example is when the individual semantic structures are represented as semantic networks of a group of actors and connected through these actors’ social ties to constitute knowledge of a social group. How to jointly represent the two types of networks is not trivial as the layers and not the nodes of the layers of the reported networks are coupled through a network on the reports. We propose to transform the different multiple networks using line graphs, where actors are affiliated with ties represented as nodes, and represent the totality of the different types of ties as a multilevel network. This affords studying the associations between the social network and the reports as well as the alignment of the reports to a criterion graph. We illustrate how the procedure can be applied to studying the social construction of knowledge in local flood management groups. Here we use multilevel exponential random graph models but the representation also lends itself to stochastic actor-oriented models, multilevel blockmodels, and any model capable of handling multilevel networks.     

Collective action and network change

Network models of collective action commonly assume fixed social networks in which ties influence participation through social rewards. This implies that only certain ties are beneficial from the view of individual actors. Accordingly, in this study we allow that actors strategically revise their relations. Moreover, in our model actors also take into account possible network consequences in their participation choices. To handle the interrelatedness of networks and participation, we introduce new equilibrium concepts. Our equilibrium analysis suggests that structures that tend to segregate contributors from free riders are stable, but costless network change only promotes all-or-nothing participation and complete networks.     

Network cohesion and social support

This article analyses the effects of personal network cohesion on different types of social support using two dimensions of cohesion: network closure (defined as a tightly knit set of actors around the ego) and cliquishness (defined as the extent to which an actor is connected with a number of cohesive sub-sets of alters). Data were obtained from a personal networks’ survey conducted in Catalonia (Spain), which was completed by 441 adults and gathered information about exchange of social support in networks made of 30 alters. A multilevel analysis disentangles the effects on support of these two structural dimensions at the network-level from compositional effects at the network and tie-level. The results show that network closure does not play a relevant role in support once confounders at the network and tie levels are controlled for. However, cliquishness has a significant association with labor-related support and housing support, net of statistical controls. Implications of these results in network research are discussed.     

Micro-structural foundations of network inequality: Evidence from a field experiment in professional networking

Sociological accounts of network inequality typically rely on the logic of preferential attachment, holding that individuals in a social network prefer to form ties with central rather than peripheral actors. We develop an alternative explanation for the growth of network inequality that does not require actors to have knowledge about the social position of others or to hold explicit preferences for partners based on such knowledge. Instead, we theorize that central actors benefit from being exposed to more opportunities for triadic closure, which confounds a quality- or popularity-based signal that their greater connectedness might also send. We test this prediction an observational study and a field experiment across multiple professional conferences. In the field experiment, we test whether network centrality is predictive of tie formation if the benefits that central actors receive through their disproportional exposure to second-order network neighbors are randomly suppressed. The findings demonstrate that for the same level of exposure to opportunities for triadic closure, central actors and less central actors are equally likely to be selected as network partners. We discuss how the proposed mechanism may be used to rectify social capital disadvantages among disadvantaged groups.     

Actor non-response in valued social networks: The impact of different non-response treatments on the stability of blockmodels

Social network data usually contain different types of errors. One of them is missing data due to actor non-response. This can seriously jeopardize the results of analyses if not appropriately treated. The impact of missing data may be more severe in valued networks where not only the presence of a tie is recorded, but also its magnitude or strength. Blockmodeling is a technique for delineating network structure. We focus on an indirect approach suitable for valued networks. Little is known about the sensitivity of valued networks to different types of measurement errors. As it is reasonable to expect that blockmodeling, with its positional outcomes, could be vulnerable to the presence of non-respondents, such errors require treatment. We examine the impacts of seven actor non-response treatments on the positions obtained when indirect blockmodeling is used. The start point for our simulation are networks whose structure is known. Three structures were considered: cohesive subgroups, core-periphery, and hierarchy. The results show that the number of non-respondents, the type of underlying blockmodel structure, and the employed treatment all have an impact on the determined partitions of actors in complex ways. Recommendations for best practices are provided.     

Avoidance in negative ties: Inhibiting closure,reciprocity, and homophily

Theorising of negative ties has focused on simplex negative tie networks or multiplex signed tie networks. We examine the fundamental differences between positive and negative tie networks measured on the same set of actors. We test six mechanisms of tie formation on face-to-face positive (affect/esteem) and negative (dislike/disesteem) networks of 282 university students. While popularity, activity, and entrainment are present in both networks, closure, reciprocity, and homophily are largely absent from negative tie networks. We argue this arises because avoidance is inherent to negative sentiments. Avoidance reduces information transfer through negative ties and short-circuits cumulative causation.     

Modelling the evolution of a bipartite network—Peer referral in interlocking directorates

A central part of relational ties between social actors is constituted by shared affiliations and events. The action of joint participation reinforces personal ties between social actors as well as mutually shared values and norms that in turn perpetuate the patterns of social action that define groups. Therefore the study of bipartite networks is central to social science. Furthermore, the dynamics of these processes suggests that bipartite networks should not be considered static structures but rather be studied over time. In order to model the evolution of bipartite networks empirically we introduce a class of models and a Bayesian inference scheme that extends previous stochastic actor-oriented models for unimodal graphs. Contemporary research on interlocking directorates provides an area of research in which it seems reasonable to apply the model. Specifically, we address the question of how tie formation, i.e. director recruitment, contributes to the structural properties of the interlocking directorate network. For boards of directors on the Stockholm stock exchange we propose that a prolific mechanism in tie formation is that of peer referral. The results indicate that such a mechanism is present, generating multiple interlocks between boards.     

Psychological predispositions and network structure: The relationship between individual predispositions,structural holes and network closure

We examine the effect of individual psychological differences on network structures, proposing several hypotheses about how individual differences might predispose actors to structure their social environment by seeking network closure or by sustaining structural holes. We introduce a new triad census method to examine personal networks of strong and weak ties. For 125 egocentric networks we correlated the triad census results with several extensively researched psychological instruments. The triad census reduced to three principal components, describing central aspects of strength-of-weak-ties and structural holes theories. Psychological predispositions explained a significant proportion of the variance in each of these components. Our results suggest that people who see themselves vulnerable to external forces tend to inhabit closed networks of weak connections. On the other hand, people who seek to keep their strong tie partners apart, and thereby bridge structural holes, tend to be individualists, to believe that they control the events in their lives, and to have higher levels of neuroticism. Finally, people with strong network closure and “weak” structural holes (as with the “strength of weak ties”) tend to categorize themselves and others in terms of group memberships. They also tend to be more extraverted and less individualistic.     

What makes a network go round? Exploring the structure of a strong component with exponential random graph models

A strong component is a subgraph in a directed network where, following the direction of ties, all nodes in the graph are reachable from one another. Mutual reachability implies that every node in the graph is theoretically able to send materials to and/or influence every other node suggesting that strong components are amongst the more egalitarian network structures. Despite this intriguing feature, they remain understudied. Using exponential random graph models (ERGM) for directed networks, we investigate the social and structural processes underlying the generation of strong components. We illustrate our argument using a network of 301 nodes and 703 personal lending ties from Renaissance Florence. ERGM shows that our strong component arises from triadic clustering alongside an absence of higher-order star structures. We contend that these processes produce a strong component with a hierarchical, rather than an egalitarian structure: while some nodes are deeply embedded in a dense network of exchange, the involvement of others is more tenuous. More generally, we argue that such tiered core-periphery strong components will predominate in networks where the social context creates conditions for an absence of preferential attachment alongside the presence of localized closure. Although disparate social processes can give rise to hierarchical strong components linked to these two structural mechanisms, in Florence they are associated with the presence of multiple dimensions of social status and the connectedness of participants across disparate network domains.     

Making Media for Themselves: Strategic Dilemmas of Prefigurative Work in Independent Media Outlets

This article explores independent youth media outlets, one sector of the broader media democracy movement, to theorize the strategic dilemmas that actors negotiate as they attempt to prefigure the media change that they want to see in the world. The outlets operate as noncommercial spaces for youth to challenge the norms of mainstream media through the collective production of oppositional media. In this millennium, we see the increased presence of these outlets across the country and globally as youth are building nodes of communication through differing digital platforms. The article draws out a case study of one independent media outlet, Youth Media Action, in an urban area in the northeastern part of the United States to trace the dynamics of how these outlets attempt to prefigure or model media change through their structures, practices, and content with a changing cast of participating youth groups. I then analyze the tensions that actors confront as they seek to model an ideal (a more just media system) within a constrained environment (overextended staff and resources). The focus on these prefigurative practices and politics affords a closer view of the ways that these sites seek to build a more inclusive, egalitarian, noncommercial media system with limited resources and educational and political practices that do not always resonate with the participating groups and youth. This research contributes to social movement work that focuses on dilemmas that groups face as they look to themselves to build and model systems of social change.     

The rationality of social structure: Cooperation in social dilemmas through investments in and returns on social capital

We develop a game-theoretic model of how social structure in the sense of a network of relations between actors helps to mitigate social dilemmas. We simultaneously endogenize the network by modeling actors’ incentives to establish the network. Since the network of relations helps to mitigate social dilemmas, it constitutes social capital. We thus analyze investments in and returns on social capital in social dilemmas and characterize the value of social capital. Our model covers a class of social dilemma games including the Trust Game, the Investment Game, the Prisoner's Dilemma, the two-actor Public Goods Game, and others.     

Longitudinal analysis of personal networks. The case of Argentinean migrants in Spain

This paper discusses and illustrates various approaches for the longitudinal analysis of personal networks (multilevel analysis, regression analysis, and SIENA). We combined the different types of analyses in a study of the changing personal networks of immigrants. Data were obtained from 25 Argentineans in Spain, who were interviewed twice in a 2-year interval. Qualitative interviews were used to estimate the amount of measurement error and to isolate important predictors. Quantitative analyses showed that the persistence of ties was explained by tie strength, network density, and alters’ country of origin and residence. Furthermore, transitivity appeared to be an important tendency, both for acquiring new contacts and for the relationships among alters. At the network level, immigrants’ networks were remarkably stable in composition and structure despite the high turnover. Clustered graphs have been used to illustrate the results. The results are discussed in light of adaptation to the host society.     

Tensions that Make a Difference: Institutions,Interests, and the Immigrant Drive1

This article examines the dynamics underlying contemporary South‐North population displacements and the common gaps between public perceptions of the phenomenon and actual realities. I analyze these dynamics and gaps by focusing on the clash between opposing forces at various stages of the migration process. These tensions occur between actors endowed with unequal power, leading to inefficient Nash‐like equilibria where the interests of less powerful participants and those of society‐at‐large in the successful integration of its newest members are not maximized. Matrices of hypothetical payoffs for relevant actors are presented for illustration. Systemic changes that would alter the present situation in the direction of more efficient and stable outcomes are discussed.     

Analysis of contested reports in exchange networks based on actors’ credibility

Exchanges of information, goods, and services are an essential part of human relations. However, a significant number of reported exchange ties tend to be contested: the reports of the sender and the receiver in an exchange do not concur with each other. To accurately understand the exchange ties between actors and the properties of the associated exchange networks, it is important to address such disagreement. Common practices either eliminate the contested reports or symmetrize them. Neither of them is ideal, as both underuse valuable information in the reports. In this paper, we propose new methods for handling contested exchange ties. The key idea is to measure actors’ credibility based on their asymmetric connections. For example, an actor is less credible the more contested ties she or he has. Using the credibility scores thus calculated, we develop two methods for handling contested ties. The first method is deterministic: it takes the report of the more credible actor as a reflection of the true exchange status between two actors. The second method is stochastic: it assumes the true exchange status between two actors is a random draw from their reports with probabilities proportional to their credibility. We illustrate the above methods by analyzing contested reports in cigarette exchange networks among middle school students in China and social and economic exchange networks among rural households in South Africa. The results show that our methods provide more reasonable corrections to contested reports than previous methods.     

Bayesian analysis for partially observed network data,missing ties,attributes and actors

We consider partially observed network data as defined in Handcock and Gile (2010). More specifically we introduce an elaboration of the Bayesian data augmentation scheme of Koskinen et al. (2010) that uses the exchange algorithm (Caimo and Friel, 2011) for inference for the exponential random graph model (ERGM) where tie variables are partly observed. We illustrate the generating of posteriors and unobserved tie-variables with empirical network data where 74% of the tie variables are unobserved under the assumption that some standard assumptions hold true. One of these assumptions is that covariates are fixed and completely observed. A likely scenario is that also covariates might only be partially observed and we propose a further extension of the data augmentation algorithm for missing attributes. We provide an illustrative example of parameter inference with nearly 30% of dyads affected by missing attributes (e.g. homophily effects). The assumption that all actors are known is another assumption that is liable to be violated so that there are “covert actors”. We briefly discuss various aspects of this problem with reference to the Sageman (2004) data set on suspected terrorists. We conclude by identifying some areas in need of further research.     

Network knowledge and the use of power

Complementing recent work on the effects of power on network perceptions, we offer a theory specifying how knowledge of network structures and exchange processes differentially affect the use of power by advantaged and disadvantaged positions. We argue that under certain conditions, network knowledge is beneficial to occupants of low-power positions, but not to occupants of high-power positions. Any low-power actor can benefit from having superior information, but if all low-power actors have equally sound knowledge, then all are worse off—a type of social trap. We tested these arguments by manipulating power and the availability of information on network structure and exchange processes in an experimental exchange network setting. The results were supportive.