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.     

Estimating network structure via random sampling: Cognitive social structures and the adaptive threshold method

This paper introduces and tests a novel methodology for measuring networks. Rather than collecting data to observe a network or several networks in full, which is typically costly or impossible, we randomly sample a portion of individuals in the network and estimate the network based on the sampled individuals’ perceptions on all possible ties. We find the methodology produces accurate estimates of social structure and network level indices in five different datasets. In order to illustrate the performance of our approach we compare its results with the traditional roster and ego network methods of data collection. Across all five datasets, our methodology outperforms these standard social network data collection methods. We offer ideas on applications of our methodology, and find it especially promising in cross-network settings.     

How social exclusion distorts social network perceptions

In two studies we investigate whether social exclusion—a well-studied, common and quite painful social experience-influences people's perceptions of novel social networks. In a first study, exclusion experiences led people to report that novel networks were more dense relative to those who had not been excluded. As predicted, this was true only for social networks; exclusion had no impact on perceptions when networks were described as geographical. In a follow-up experiment, participants watched a custom-created video, depicting avatars interacting in social scenes and they were asked to report the ties among the avatars in the video. Exclusion experiences led people to see network ties where none exist (i.e., false positives), though there was no effect for exclusion (versus inclusion) on reports of false negatives. Results indicate that common social experiences systematically shape network perceptions, leading people to seeing novel social networks as more densely connected than they are.     

Network recall among older adults with cognitive impairments

Although it is widely accepted that personal networks influence health and illness, network recall remains a major concern. This concern is heightened when studying a population that is vulnerable to cognitive decline. Given these issues, we use data from the Social Network in Alzheimer Disease project to explore similarities and discrepancies between the network perceptions of focal participants and study partners. By leveraging data on a sample of older adults with normal cognition, mild cognitive impairment, and early stage dementia, we explore how cognitive impairment influences older adults’ perceptions of their personal networks. We find that the average individual is more likely to omit weaker, peripheral ties from their self-reported networks than stronger, central ties. Despite observing only moderate levels of focal-partner corroboration across our sample, we find minimal evidence of perceptual differences across diagnostic groups. We offer two broad conclusions. First, self-reported network data, though imperfect, offer a reasonable account of the core people in one’s life. Second, our findings assuage concerns that cognitively impaired older adults have skewed perceptions of their personal 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.     

The integrative power of online collective action networks beyond protest. Exploring social media use in the process of institutionalization

Abstract

In this article, we aim at expanding the event-based and protest-centered perspective that is typically adopted to study the nexus between social media and movements. To this aim, we propose a network-based approach to explore the changing role that these tools play during the dynamic unfolding of movement processes and, more particularly, over the course of their institutionalization. In the first part, we read the added value of social media as a function of the ‘integrative power’ of the networks they foster – a unique and evolving form of sociotechnical power that springs from the virtuous encounter between social media networking potential and social resources. In the second part, we investigate this form of power by focusing directly on online networks’ structure as well as on the type of communication and participation environments they host. We apply our proposed approach to the longitudinal exploration of the Twitter networks deployed in the period 2012–2014 during three annual editions of the transnational feminist campaign ‘Take Back The Tech!’ (TBTT). Results from our case study suggest that, over time, TBTT supporters do in fact make a differentiated use of social media affordances – progressively switching their communicative strategies to better sustain the campaign’s efforts inside and outside institutional venues. Thus, the exploration of the TBTT case provides evidence of the usefulness of the proposed approach to reflect on the different modes in which social media can be exploited in different mobilization stages and political terrains.     

Omission and commission errors in network cognition and network estimation using ROC curve

Network studies on cognitive social structures collect relational data on respondents’ direct ties and their perception of ties among all other individuals in the network. When reporting their perception networks, respondents commit two types of errors, namely, omission (false negatives) and commission (false positives) errors. We first assess the relationship between these two error types, and their contributions on overall respondent accuracy. Next we propose a method for estimating networks based on perceptions of a random sample of respondents from a bounded social network, which utilizes the receiver operator characteristic curve for balancing the tradeoffs between omission and commission errors.     

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.     

A latent space model for cognitive social structures data

This paper introduces a novel approach for modeling a set of directed, binary networks in the context of cognitive social structures (CSSs) data. We adopt a relativist approach in which no assumption is made about the existence of an underlying true network. More specifically, we rely on a generalized linear model that incorporates a bilinear structure to model transitivity effects within networks, and a hierarchical specification on the bilinear effects to borrow information across networks. This is a spatial model, in which the perception of each individual about the strength of the relationships can be explained by the perceived position of the actors (themselves and others) on a latent social space. A key goal of the model is to provide a mechanism to formally assess the agreement between each actors’ perception of their own social roles with that of the rest of the group. Our experiments with both real and simulated data show that the capabilities of our model are comparable with or, even superior to, other models for CSS data reported in the literature.     

Network stability in organizational flux: The case of in-house management consulting

This article explores the effects of organizational flux on the statics of interpersonal knowledge exchange structures. In our empirical case, organizational flux refers to high rates of personnel turnover, temporary project work, constant recombination of team memberships, and short-term collaboration. Network statics refers to the balance between forces composing and decomposing a network. Set in continuously changing organizational compositions of an in-house management-consulting unit, our study offers insights into the relationship between organizational characteristics and informal networks between experts. Against conventional intuition, we find a dense and stable structure of knowledge exchange. Furthermore, we elaborate on the two concepts of organizational flux and network statics, by comparing the observed structures with theoretical (random graphs) and empirical references (other empirical cases), and by using ERG models to account for different factors of organization, social balance, and individual attributes.     

Social learning about levels of perinatal and infant mortality in Niakhar,Senegal

How individuals develop perceptions concerning the risk of infant and child mortality has important consequences for fertility and demographic transition theory and for understanding broader processes of social learning. The role of learning through social interaction in shaping demographic phenomena has been the subject of intense research in the last decade. Much previous research however has been hampered by inadequate measures of individuals’ personal networks, the proximal context in which learning takes place. Using pilot data employing an innovative social network design in conjunction with demographic surveillance data from Niakhar, Senegal, this research models perception of change in the level of infant mortality over time as a function of the experience of social network associates with perinatal and infant mortality. Results suggest relatively strong effects of network members’ mortality experience controlling for own experiences of child mortality as well as neighborhood and community levels of infant mortality among other controls.     

Class inequalities in access to social capital in the metropolitan area of Buenos Aires

this paper analyzes social class inequalities in access to social capital. Quantitative methodology was used with data from the PI-Clases “Reproduction and social mobility in family trajectories and life courses” survey, conducted in the Metropolitan Area of Buenos Aires in 2015 (n = 1065). Social capital measures where derived from the Position Generator (Lin, 2001) to identify the volume, status and range of the respondent’s networks. Results show that there are class inequalities in access to social capital in terms of the number of contacts, mean status of contacts, range of contact’s status and highest status of contacts. Also, the working class is more heterogeneous in its access to social capital, suggesting it has a sector that has network ties with people from the middle class and a sector with lower social capital. The observed trend is of gradational differences between class strata, with more marked inequalities between the service, intermediate and working class, but with fluid class boundaries without social segregation. Also, social capital is conditioned by intergenerational mobility trajectory, showing a trend of elasticity in the class composition of social relationships. The upwardly mobile can increase their access to social capital but don’t reach the same level as the intergenerationally stable, while the downwardly mobile retain some social capital from their class origins.     

话语权的回归:论网络语境下的青年影像

在传统媒介环境下,青年影像主要从内容和接受者两个角度界定。年轻人是被叙述者,而不是叙述者。传统媒介环境下的青年影像是一种"伪青年"文化。网络激发了人们特别是年轻人的参与热情。年轻人作为自为者,而不是他为者或为他者而存在。网络青年影像是一种真正的青年文化,可以粗略地分为校园和后校园影像。网络青年影像的意义在于它的不确定性,由此呈现出向未来开放的文化特性。     

Statistical power of the social network autocorrelation model

The network autocorrelation model has become an increasingly popular tool for conducting social network analysis. More and more researchers, however, have documented evidence of a systematic negative bias in the estimation of the network effect (ρ). In this paper, we take a different approach to the problem by investigating conditions under which, despite the underestimation bias, a network effect can still be detected by the network autocorrelation model. Using simulations, we find that moderately-sized network effects (e.g., ρ = .3) are still often detectable in modest-sized networks (i.e., 40 or more nodes). Analyses reveal that statistical power is primarily a nonlinear function of network effect size (ρ) and network size (N), although both of these factors can interact with network density and network structure to impair power under certain rare conditions. We conclude by discussing implications of these findings and guidelines for users of the autocorrelation model.     

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.     

Prismatic effects of negative ties

Research on negative ties has focused primarily on the harm they do. In this paper, we show that negative ties can also have beneficial effects. We argue that, like positive ties, negative ties can link actors together in the minds of observers. As a result, we theorize that negative ties with high-status actors can benefit a focal actor, whereas negative ties with low-status actors can harm the focal actor. This prismatic effect depends on the existing status of the focal actor: a focal actor of low status is likely to benefit far more from negative ties with high-status actors and suffer more from negative ties with low-status actors than will an actor of high-status. To test our ideas, we analyze the phenomenon of "diss songs" in hip-hop music. A diss song is a song in which a rapper makes derogatory comments about another rapper, constituting a negative tie. We analyze the effects of negative ties among 53 rappers over 20 points in time on audience reaction as measured by record sales. We find that negative ties with high-status actors enhance future sales for low-status actors. However, negative ties with lower-status actors have no effect on the future sales of both low- and high-status actors. Just as some researchers have reported both positive and negative consequences of social capital, our study demonstrates that negative ties can also have both positive and negative outcomes.     

Review of the effects of Five Factor Model personality traits on network structures and perceptions of structure

We review recent research on Five Factor Model personality and social network analysis to assess how structures develop and are perceived. Extraversion and agreeableness relate consistently to personal but not workplace networks. Extraverts are more likely to seek connections, whereas agreeable individuals receive connections from others. Openness predicts network diversity and is marginally related to position when groups pursue collective goals. Conscientiousness is associated with maintaining certain personal relationships, but is strongly related to central positions in workplace networks. Neuroticism has no consistent relationship with network size or composition, and is differentially related to network positions, depending on the context.     

Symbolic interactionism and the concept of power

Symbolic interactionism is often represented as a perspective which is limited by its restriction to 'micro' aspects of social organization. As such, it is allegedly unable to adequately conceptualize 'macro' phenomena such as social structure, patterns of inequality, and power. Such a view is routinely presented in undergraduate textbooks. This paper contests such a view through a consideration of the concept of power. We argue that the interactionist research tradition does show a fundamental concern with power phenomena, and that a reconsideration of the concept is timely in light of theoretical developments in sociology more generally. An increasing concern with the analysis of culture, the continuing influence of Foucault, the development of feminist perspectives, and the emerging consensus around neo-Weberian thought have all contributed to a renewal of interest in themes long ago explored by interactionists. As examples we suggest that interactionist studies in the fields of deviance and education have been concerned above all with the authoritative imposition of consequential identities, i.e., with the social processes through which power is enacted and institutionalized in real situations. Such developments have led some to argue that interactionism has now been incorporated into the mainstream of sociology. We conclude, however, by arguing that such a view runs the risk of granting to orthodox sociological thought a legitimacy which is analytically unwarranted, and which fails to recognize the alternative theoretical and philosophical foundations of symbolic interactionist thought.     

Network visualization and problem-solving support: A cognitive fit study

This study examines the relative effectiveness of four different social network representations for improving human problem-solving accuracy and speed: node-link diagrams, adjacency matrices, tables, and text. Results suggest that visual network representations improve problem-solving accuracy and speed, compared with text. Among the visual representations, tables produced superior problem-solving outcomes for symbolic tasks and link-node diagrams produced superior problem-solving outcomes for spatial tasks. These results partially support a cognitive fit model of problem-solving support. There is not “one best way” to represent network data. Instead, it is important to match network representations and problem-solving tasks.     

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.