The stability of exchange networks

Economic and sociological exchange theories predict divisions of exchange benefits given an assumed fixed network of exchange relations. Since network structure has been found to have a large impact on actors’ payoffs, actors have strong incentives for network change. We answer the question what happens to both the network structure and actor payoffs when myopic actors change their links in order to maximize their payoffs. We investigate the networks that are stable, the networks that are efficient or egalitarian with varying tie costs, and the occurrence of social dilemmas. Only few networks are stable over a wide range of tie costs, and all of them can be divided into two types: efficient networks consisting of only dyads and at most one isolate, and Pareto efficient and egalitarian cycles with an odd number of actors. Social dilemmas are observed in even-sized networks at low tie costs.     

Theories of network exchange: Anomalies,desirable properties,and critical networks

The present study evaluates four well-known theories of exchange in networks. In previous research these theories’ predictions were compared for a small set of networks using experimental data. We compared their predictions for all 12,112 networks up to size 8. By comparing these predictions we (i) identified anomalies in theories of network exchange, (ii) investigated to what extent the theories satisfy basic principles of exchange, (iii) identified ‘critical’ networks for which predictions are very different. We conclude that exchange in networks is not yet well understood.     

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.     

Power laws and fragility in flow networks

What makes economic and ecological networks so unlike other highly skewed networks in their tendency toward turbulence and collapse? Here, we explore the consequences of a defining feature of these networks: their nodes are tied together by flow. We show that flow networks tend to the power law degree distribution (PLDD) due to a self-reinforcing process involving position within the global network structure, and thus present the first random graph model for PLDDs that does not depend on a rich-get-richer function of nodal degree. We also show that in contrast to non-flow networks, PLDD flow networks are dramatically more vulnerable to catastrophic failure than non-PLDD flow networks, a finding with potential explanatory power in our age of resource- and financial-interdependence and turbulence.     

Analyzing large scale exchange networks

Exchange theories or their implementations in algorithms have limited utility because they can be applied only to quite small networks. They cannot be applied to larger networks until that size limit is removed. Domain Analysis cuts networks into smaller pieces at the boundaries of strong power domains. Domain Analysis identifies strong power and breaks, and distinguishes domains that function exactly as they would were they free-standing, and components that do not. Support for the finding of breaks and the distinction between domains and components are obtained using both experimental data and simulations based on X-Net. To illustrate the use of Domain Analysis, it is applied to find the incidence of strong power in large exchange networks. The application shows that the incidence of strong power decreases as network density increases, and that strong power occurs only infrequently in dense networks. We conclude by calling for ever more general analytic procedures.     

One-shot exchange networks and the shadow of the future

From the Prisoner's Dilemma and other games, it is well known that strategy selection in one-shot games can be very different from that in iterated games. Because exchange structures were studied only as iterated games, whether one-shot structures differ was not known. Nor have exchange theories previously considered whether events in structures would be different if studied as one-shot games. This paper offers new theory to predict one-shot exchange structures and one-shot experiments to test that theory. As predicted, the experiments found that processes and outcomes of one-shot exchange structures are quite different from those of iterated exchange structures. For example, certain relations that are strategically used as threats in iterated exchange structures occur very rarely in one-shot structures. It follows that power differences in one-shot structures regress from those observed for repeated structures.     

The structure of online social networks mirrors those in the offline world

We use data on frequencies of bi-directional posts to define edges (or relationships) in two Facebook datasets and a Twitter dataset and use these to create ego-centric social networks. We explore the internal structure of these networks to determine whether they have the same kind of layered structure as has been found in offline face-to-face networks (which have a distinctively scaled structure with successively inclusive layers at 5, 15, 50 and 150 alters). The two Facebook datasets are best described by a four-layer structure and the Twitter dataset by a five-layer structure. The absolute sizes of these layers and the mean frequencies of contact with alters within each layer match very closely the observed values from offline networks. In addition, all three datasets reveal the existence of an innermost network layer at ∼1.5 alters. Our analyses thus confirm the existence of the layered structure of ego-centric social networks with a very much larger sample (in total, >185,000 egos) than those previously used to describe them, as well as identifying the existence of an additional network layer whose existence was only hypothesised in offline social networks. In addition, our analyses indicate that online communities have very similar structural characteristics to offline face-to-face networks.     

Duocentered networks

When a pair of individuals is central to a research problem (e.g., husband and wife, PhD student and supervisor) the concept of “duocentered” networks can be defined as a useful extension of egocentered networks. This new structure consists of a pair of central egos and their direct links with alters, instead of just one central ego as in the egocentered networks or multiple egos as in complete networks. The key point in this kind of network is that ties exist between the central pair of egos and between them and all alters, but the ties among alters are not considered. Duocentered networks can also be considered as a compromise between egocentered and complete networks. Complete network measurements are often costly to obtain and tend to contain a large proportion of missing data (especially for peripheral actors). Egocentered network data are less costly but a lot of information is lost with their use when a pair of individuals is the relevant unit of analysis.     

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.     

Understanding the self-organization of occupational sex segregation with mobility networks

Decades after the beginning of the gender revolution, most women and men still work in sex-typed occupations. This is a primary driver of the gender wage gap. Most research describing the patterns of occupational sex segregation focuses on supposedly innate job characteristics that match gender stereotypical abilities and preferences, such as the use of mathematical skills or social skills, on income and status differences between occupations, and on organizational job characteristics, for example, the need to work long hours. However, beyond such occupational attributes, sex segregation is hypothesized to exhibit emergent patterns that are linked to the interdependent job mobility of women and men, in particular, men selectively leaving feminizing occupations. Developing new tools inspired by statistical network research, and using representative, longitudinal data that contain detailed occupational mobility from the UK between 2000 and 2008, this replacement mechanism is analyzed. It is shown that 19–28% of observed sex segregation is linked to this emergent phenomenon in a statistical model that disentangles the various predictors of the allocation of women and men to different occupations. This makes it the most important predictor of segregation in contrast to concurrently modelled explanations based on occupational characteristics.Data and materials availabilityThe BHPS data and the LFS data are available from the UK Data Service (https://www.ukdataservice.ac.uk/). The O*NET data is available from the O*NET homepage (https://www.onetcenter.org/). Software implemented in the environment R and code for data analysis are available upon request from the authors.     

Staying connected under fire: Effects of individual roles and organizational specialization on the robustness of emergency-phase communication networks

Communication network connectivity is central to organizational performance, but maintaining connectivity can be difficult during periods of disruption. During the World Trade Center (WTC) disaster of September 11th, 2001, both emergency response-specialized organizations and organizations without such specialization forcibly adapted to a radically altered environment. Their dynamic communication networks necessarily entailed trade-offs between competing demands for efficiency and robustness to disruption. Of particular importance is whether organizations concentrated activity within a small number of “hub” nodes, and whether those with existing coordinative roles were critical to maintaining connectivity. We examine these questions by analyzing seventeen organizational communication networks in the WTC disaster. We find that organizations maintain connectivity through relatively small numbers of coordinators, but the realization of institutionalized coordinative roles depends upon organizational context. Further, we find distinct patterns of robustness, with the removal of key players leading to mass isolation in specialist networks versus a gradual pattern of failure in non-specialist networks. These results suggest that organizations responding to disruptions within their usual domain of operations will be more likely to retain institutionalized roles when building emergent networks, although this increases the network's fragility in the event of the loss of one of those coordinators. Organizations responding to novel threats may reconfigure more radically, limiting vulnerability to the loss of institutionalized—but not emergent—coordinators.     

A concept for measuring network proximity of regions in R&D networks

This paper proposes a new measure for assessing the network proximity between aggregated units, based on disaggregated information on the network distance of actors. Specific focus is on R&D network structures between regions. We introduce a weighted version of the proximity measure, related to the idea that direct and indirect linkages carry different types of knowledge. First-order proximity arising from direct cross-regional linkages is distinguished from higher-order network proximity, resulting from indirect linkages in the R&D network. We use an macroeconomic application in which we analyse the productivity effects of R&D network spillovers across regions to illustrate the usefulness of a proximity measure for aggregated units.     

Power and the perception of social networks

Previous work has led to divergent conclusions about how power affects the accuracy of network perceptions in groups and organizations. This paper develops and tests an argument linking higher power to less accurate network perception. Results from the first experiment showed that, relative to participants primed with high power, those primed with low power had more accurate perceptions of who was tied to whom in novel networks. The second experiment demonstrated that such differences in perceptual accuracy do not emerge for non-social relations.     

In transit: Changing social networks of sub-Saharan African migrants in Turkey and Greece

Research on the role of social networks in human migration has mainly relied on single snapshots in time. This paper focuses on the changes in composition and usage of the transnational networks of migrants and why these changes occur. It is based on ethnography and network analysis with forty sub-Saharan African migrants in two transit contexts: Turkey and Greece, over a 17-month period. Findings show that relationship preferences, resources and communication infrastructures constitute an individual opportunity infrastructure affecting how critical events produce network changes. This process is ongoing through the continued experience of critical events, suggesting that the role of networks fluctuates over time.     

Ethnic segregation of friendship networks in school: Testing a rational-choice argument of differences in ethnic homophily between classroom- and grade-level networks

Adolescents’ school-based friendship networks tend to be segregated along ethnic lines. But few studies have examined whether variation in network boundaries affects the degree of ethnic friendship segregation. We use rational-choice theory to argue that ethnic homophily is more pronounced for friendships between classrooms than for those within classrooms. We empirically test this hypothesis using two-wave German panel data (N = 1258) and stochastic actor-oriented models (RSiena). In line with our theoretical argument, we find that the tendency to form same-ethnic friendships is indeed stronger at the grade level, which translates into stronger ethnic segregation in friendship networks at the grade level than at the classroom level. Implications for research on ethnic segregation in school-based friendship networks are discussed.     

A comparative study of social network models: Network evolution models and nodal attribute models

This paper reviews, classifies and compares recent models for social networks that have mainly been published within the physics-oriented complex networks literature. The models fall into two categories: those in which the addition of new links is dependent on the (typically local) network structure (network evolution models, NEMs), and those in which links are generated based only on nodal attributes (nodal attribute models, NAMs). An exponential random graph model (ERGM) with structural dependencies is included for comparison. We fit models from each of these categories to two empirical acquaintance networks with respect to basic network properties. We compare higher order structures in the resulting networks with those in the data, with the aim of determining which models produce the most realistic network structure with respect to degree distributions, assortativity, clustering spectra, geodesic path distributions, and community structure (subgroups with dense internal connections). We find that the nodal attribute models successfully produce assortative networks and very clear community structure. However, they generate unrealistic clustering spectra and peaked degree distributions that do not match empirical data on large social networks. On the other hand, many of the network evolution models produce degree distributions and clustering spectra that agree more closely with data. They also generate assortative networks and community structure, although often not to the same extent as in the data. The ERGM model, which turned out to be near-degenerate in the parameter region best fitting our data, produces the weakest community structure.     

Random alliance networks

The morphological properties of kinship and marriage alliance networks, such as circuits, are typically considered as indicators of sociological phenomena — yet, they may also be partly coincidental. To assert the contribution of chance to these morphological features, we develop a standardized method where empirical alliance networks are compared with a random baseline. We apply our framework to a variety of empirical cases and show that some corpuses are remarkably well reconstructed by our random model, while others still feature significant divergencies which may be partly connected to field-based experience. On the whole, our approach may be used to scrutinize the matrimonial role of social groups as asserted by native or ethnological theory.     

Upward contacts in everyday life: Benefits of reaching hierarchical relations in ego-centered networks

Recent studies have inspired inquiries about what circumstances allow people to gain from interactions with those who rank higher than themselves in the social hierarchy. We examine how self-reported benefits of such upward contacts vary by tie strength and network structures in everyday life. Data were drawn from contact diaries that 137 individuals recorded over seven months in 2014; these diaries captured unique features of 94,353 one-on-one contacts that 137 diary keepers made, along with the estimated tie strength and the extent of embeddedness among network members. Multilevel models with interaction terms show that diary keepers benefit from contacts with people who play higher hierarchical roles and that the benefits become more substantial when the higher-ranked others are weakly tied to the diary keepers and connected with fewer fellow network members. The paper extends contact diary studies to estimate alter-alter ties that help construct comprehensive structures in egocentric networks.     

Identifying regular blocks in valued networks: A heuristic applied to the St. Marks carbon flow data,and international trade in cereal products

While the concept of regular equivalence is equally applicable to dichotomous as well as valued networks, the identification of regular blocks in regular blockmodels is somewhat problematic when dealing with valued networks. Applying the standard procedure for identifying ties in such blockmodels, a procedure perhaps most suited for dichotomous networks, does tend to generate block images and reduced graphs that differ from intuitive notions of such structures.     

Transitions of women’s substance use recovery networks and 12-month sobriety outcomes

Little is known about how the structure and composition of women’s personal social networks (PSNs) combine to support recovery from substance use disorders, how PSNs change during early recovery, or how known covariates such as trauma, co-occurring mental health disorders, or treatment modality impact this relationship. This study used latent profile and transition analyses with 6 recovery-specific PSN indicators in a sample of women in early recovery (N = 377) to identify three PSN typologies in relation to abstinence outcomes over 12 months, and track transitions between the typologies at 0–6 and 6–12 months. Women in the Highly Connected type (14.3 %) had tightly-knit networks, more sober alters, and fewer treatment-related alters. Women in the Treatment-Related type (49.3 %) had looser-knit networks with more sober and sobriety-supporting alters and alters they know from treatment. Women in the At-Risk type (36.3 %) had more isolates, few sobriety-supporting alters, and more alters with whom they used. Women in the Treatment-Related Sobriety Support type were significantly more likely to maintain sobriety by 12 months (B = −0.81; OR = 2.09, 95 % CI [1.23−3.56]) than women in the At Risk type. Higher mean Trauma Symptom Checklist scores were positively related to membership in the At Risk type. The majority of women who transitioned did so by 6 months, with 41.6 % transitioning then. Women in the At Risk group had the highest probability of transition (P = 0.55). Being in residential treatment (versus outpatient) predicted lower odds of transitioning (B = −0.81, p = .06). This study provides a framework for conducting longitudinal latent variable analysis with social network data, and offers a clinically-useful starting point for research on individualized, targeted, and stage-based interventions for women in recovery.