Public diplomacy networks: China’s public diplomacy communication practices in twitter during Two Sessions

This study illustrates the Two Sessions and proposes the concept of public diplomacy network to investigate the effect of China’s public diplomacy communication on Twitter. Twitter data comprising 14,874 tweets and 7773 users was collected during March 5 to March 20 in 2018, the combination of social network analysis and content analysis were employed from a holistic, relational and networked approach. The results present public diplomacy networks’ structural characteristics, key participants’ networking patterns, communication strategies, issue participation, and homophily effect that participants who take the same attitude towards China (negative & neutral or positive) are clustered in each group. In conclusion, although China’s state-owed media takes the lead in public diplomacy communication, foreign practitioners dominate the NGOs, researchers, correspondents, and generally take negative attitude towards China. China’s media outlets underperform in allying closely, and China is entrenched in traditional one-way monologue. The theoretical, methodological and practical implications are addressed.     

The missing link: Social network analysis in migration and transnationalism

The focus on social networks in migration studies marked a significant departure of understanding. Social networks are not only a mechanism through which the migration process is patterned, but they also have broader implications for migrants and non-migrants alike. Despite the fact that the network character of migration processes has long been recognized in migration studies, for a long time, Social Network Analysis has not been applied. Taking this scholarly omission as a starting point, we seek in this special issue to discuss recent research into social networks and migration that use SNA approaches.     

Using network analysis to conduct a system-wide program evaluation within a university

ObjectiveTo conduct a system-wide assessment using social network analysis (SNA) to examine how 14 important issues (e.g., consent; racism) are addressed through education, training, and programming at a university.MethodsEvaluators conducted interviews with campus departments responsible for educating/training on the 14 issues. Interviews revealed which programs (n = 52) were offered that addressed the 14 issues, and data on audience characteristics, date of delivery, and which issues were covered in each program were collected. SNA was used to calculate degree and create visualization graphs illustrating patterns of content-coverage across all 52 programs.ResultsThe average degree was 19.38 (SD = 9.70), meaning programs overlapped in topic area with nearly 20 other programs, on average. Most programs (n = 36; 69.2 %) were attended by audiences of 500 people or less. “Diversity and inclusion” represented the topic area with the most programs (n = 23), whereas “suicide” and “bullying/hazing” had the least number of programs (n = 3). Degree was negatively correlated with attendance numbers (r=-.310, p < .001), indicating the more a program overlapped in content with other programs, the smaller the audience.ConclusionsThis study supports the use of network analysis in conducting systemic evaluations of programs offered at a university, complementing the work of ongoing, local-level program evaluations.     

Board interlocking network and the design of executive compensation packages

The standard approach used to model interlocks in the business and management literature is to treat each interlock of a network as an independent data point. However, such an approach ignores the complex inter-dependencies among the common director interlocks. We propose that an interlocking board network is an important inter-corporate setting that has bearing on how company boards make corporate decisions. Using a sample of 725 large U.S.-based public companies over the period 2007–2010, board member information, executive compensation information, and exponential random graph modeling (ERGM) techniques for social networks, we present evidence that board interlocks are positively linked with similarities in the design of executive compensation packages in interlocked firms, particularly the proportions of the options component. We also find evidence that board interlocks are positively linked with similarities in a number of board characteristics.     

Social network analysis: New ethical approaches through collective reflexivity. Introduction to the special issue of Social Networks

Research in social network analysis (SNA) faces unprecedented ethical challenges today due to both technological developments (‘big’ data) and a growing tendency toward institutionalization of ethics governance. We argue that a suitable response requires a more comprehensive approach to SNA ethics, and we identify two main paths toward this goal. First, we need to recognize the breadth of the subject, no longer limited to anonymity and consent. Second, we need to encourage the active participation and contribution of SNA scholars. We advocate a need for greater reflexivity at both individual and collective levels, whereby researchers can learn from their own and their peers’ experiences, and foster a more ethically ‘virtuous’ approach within the whole SNA community.     

Neighborhood isolation in Chicago: Violent crime effects on structural isolation and homophily in inter-neighborhood commuting networks

Urban sociologists and criminologists have long been interested in the link between neighborhood isolation and crime. Yet studies have focused predominantly on the internal dimension of social isolation (i.e., increased social disorganization and insufficient jobs and opportunities). This study highlights the need to assess the external dimension of neighborhood isolation, the disconnectedness from other neighborhoods in the city. Analyses of Chicago's neighborhood commuting network over twelve years (2002–2013) showed that violence predicted network isolation. Moreover, pairwise similarity in neighborhood violence predicted commuting ties, supporting homophily expectations. Violence homophily affected tie formation most, while neighborhood violence was important in dissolving ties.     

A behavior genetic analysis of the tendency for youth to associate according to GPA

Behavior genetic research has revealed that many “environmental” variables are partially influenced by genetic factors. Known as gene–environment correlation (rGE), this line of scholarship provides insight on how and why individuals select into certain environments. Juxtaposing this body of evidence with research on peer group homophily—the tendency for peers to resemble one another on certain traits such as academic ability—raised two research hypotheses: (1) youth will associate with peers who receive grades similar to themselves (i.e., homophily for GPA); and (2) a portion of the variance in peer group GPA (i.e., the peer network average GPA) will be explained by individuals’ genetic self-selection into the peer group (rGE). The results supported both hypotheses by showing a strong predictive relationship between the target individual's GPA and that of his/her peers and by revealing that 72% of the variance in peer group GPA was explained by genetic influences.     

Collaboration and competition in a children's health initiative coalition: a network analysis

Activating communities to achieve public health change and initiate policy reform usually requires collective action from many entities. This case study analyzes inter-organizational networks among members of a coalition created to expand health insurance coverage to uninsured children in a large metropolitan area. Six networks were measured: collaboration, competition, formal agreements, receive funding from, send funding to, and greater communication. The response rate was 65.8% (50 of the 76 active members). Positive network questions such as “who do you collaborate with” elicited many network choices whereas negative ones such as “who do you compete with” elicited few. The collaboration network had a core–periphery structure and analysis showed that a large network can be reduced to a small set of core organizations one-sixth the size of the whole. Centrality (out- and in-degree) was associated with perceived organizational function and perceived barriers to success. For example, organizations that received many choices as collaboration partners were more likely to perceive the coalition functioned well than those who received few choices. The study suggests that perceptions of organizational performance are associated with position in the network, central members are more likely to perceive the organization performs well than those on the periphery.     

The coevolution of trade agreements and investment treaties: Some evidence from network analysis

Regional trade agreements (RTAs) and bilateral investment treaties (BITs) are expected to promote trade and investment relationships. One critical feature of such agreements is the network, so the multiplex coevolution of RTAs and BITs should be captured by the dynamics of their two networks. Although many studies have examined the roles of RTAs and BITs, most studies do not account for crucial network properties. This study explores how RTAs and BITs coevolve by applying a stochastic actor-oriented model of multiplex network evolution. In particular, we examine the roles of (i) cross-network dyadic interinfluences and (ii) within- and cross-network preferential attachments to discuss the dynamic relationships between RTAs and BITs. The results are as follows. First, our estimation supports cross-network dyadic interinfluences. Countries that sign a BIT are willing to establish an RTA, while those that sign an RTA are reluctant to establish a BIT. Second, concerning preferential attachments, countries prefer to sign BITs with partners that have more RTA and BIT links. However, countries tend to form RTAs with partners that have more BIT links but are reluctant to form RTAs with those that have more RTA links. We discuss possible justifications for these results, including arguments regarding the benefits and costs associated with the formation of RTAs and BITs.     

Assessing organizational capacity for diffusion: A school-based social network analysis case study

The purposeful design of social networks is increasingly recognized as a fundamental organizational improvement strategy. In the PK-12 education sector, school-based teacher collaboration is the primary vehicle through which educators are able to gain access to essential social capital, and through which leaders promulgate diffusion of innovation and continuous organizational learning. In partnership with school administrators, the authors undertook an evaluation to examine the size, structure, and composition of school-based networks. Social network analysis (SNA) was used to measure and visualize connections (or lack thereof) of ties between teams and between educators. Isolate and disconnected network actors were revealed through visual inspection of the sociograms. Administrators used findings to reconfigure team membership to enhance teacher ability to give and receive support and collaboratively problem-solve, and to ensure greater capacity for diffusion of instructional innovation and organizational learning. This paper contributes to the field’s understanding of how evaluators and organizational leaders can use SNA to measure, visualize, and more purposefully design effective patterns of connection between people through which professional knowledge, support, and innovation will travel.     

Conditionally exponential random models for individual properties and network structures: Method and application

Exponential random models have been widely adopted as a general probabilistic framework for complex networks and recently extended to embrace broader statistical settings such as dynamic networks, valued networks or two-mode networks. Our aim is to provide a further step into the generalization of this class of models by considering sample spaces which involve both families of networks and nodal properties verifying combinatorial constraints. We propose a class of probabilistic models for the joint distribution of nodal properties (demographic and behavioral characteristics) and network structures (friendship and professional partnership). It results in a general and flexible modeling framework to account for homophily in social structures. We present a Bayesian estimation method based on the full characterization of their sample spaces by systems of linear constraints. This provides an exact simulation scheme to sample from the likelihood, based on linear programming techniques. After a detailed analysis of the proposed statistical methodology, we illustrate our approach with an empirical analysis of co-authorship of journal articles in the field of neuroscience between 2009 and 2013.     

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.     

The interplay between formal project memberships and informal advice seeking in knowledge-intensive firms: A multilevel network approach

In this study we investigate the interplay between knowledge workers’ formal project team memberships and their informal interactions from a multilevel network perspective. Conceptualizing knowledge workers’ affiliation with project teams as a membership network and their interactions as an advice network, we discuss how shared project team memberships as well as multiple memberships influence patterns of informal exchange in knowledge-intensive organizations. To empirically determine the impact of formal organization on informal exchange we apply exponential random graph models for multilevel networks to relational data collected on 434 R&D employees working on 218 project teams in a high-tech firm in Germany. Our results show that employees sharing project memberships create advice ties to each other but do not exchange advice reciprocally. In addition, we find a negative relationship between having a high number of project memberships and informally seeking or providing advice.     

The NEGOPY network analysis program

A method and its associated computer program for (specifically communication) network analysis are described. The program described here, NEGOPY, is relational, or linkage-based. The conceptual orientation, computational algorithm, operating characteristics, format and availability of NEGOPY are described. Finally, a partial bibliography of works describing other aspects of NEGOPY and research studies using NEGOPY is included.     

Kudos make you run! How runners influence each other on the online social network Strava

Strava is the largest online social network for athletes. We used Strava’s big data to investigate how runners in the same virtual Strava club influenced each other’s running behavior. We hypothesized that receiving kudos on recorded activities would spur exercise rates, and that the running behavior of clubmates to whom ego gave kudos would serve as a motivational example. We focused on five different Strava clubs that functioned as a virtual extension of real-life Dutch running clubs with a total of 329 members. Using data on kudos and recorded activities, we constructed a longitudinal dataset of complete networks and behavior over 11 periods with a one-month time window. We tested our hypotheses using SIENA. We found that receiving kudos induced runners to run more and more often. Moreover, athletes tended to adjust their running behavior to that of their ‘kudos-friends’ (i.e., those to whom they gave kudos). Contrary to our expectation, kudos-friends who ran more and more often than ego were not the most influential. If anything, the reverse was true; athletes were more likely to come to resemble the running behavior of their kudos-friends who ran less and less often.     

Motivations for self-assembling into project teams

This study investigates the self-assembly mechanisms of ad hoc project teams using a bipartite network perspective. Individuals and projects are modeled as two types of nodes and team membership as relations between them. This approach enables us to investigate factors that impact voluntary team assembly at the individual, dyadic, and team levels simultaneously. Using Exponential Random Graph Models (ERGM/p*), we study players’ combat teams in a Massively Multiplayer Online Role-Playing Game (MMORPG) as a case of self-assembled project teams. Empirical results show that individuals are motivated to join ad hoc teams to complete difficult projects but not projects with long durations. We also found that individuals tend to collaborate with specific teammates who have complementary skills, those who have similar age or skill level, and those who are affiliated with the same organizational entity.     

Exploring the stability of communication network metrics in a dynamic nursing context

Network stability is of increasing interest to researchers as they try to understand the dynamic processes by which social networks form and evolve. Because hospital patient care units (PCUs) need flexibility to adapt to environmental changes (Vardaman et al., 2012), their networks are unlikely to be uniformly stable and will evolve over time. This study aimed to identify a metric (or set of metrics) sufficiently stable to apply to PCU staff information sharing and advice seeking communication networks over time. Using Coefficient of Variation, we assessed both Across Time Stability (ATS) and Global Stability over four data collection times (Baseline and 1, 4, and 7 months later). When metrics were stable using both methods, we considered them “super stable.” Nine metrics met that criterion (Node Set Size, Average Distance, Clustering Coefficient, Density, Weighted Density, Diffusion, Total Degree Centrality, Betweenness Centrality, and Eigenvector Centrality). Unstable metrics included Hierarchy, Fragmentation, Isolate Count, and Clique Count. We also examined the effect of staff members’ confidence in the information obtained from other staff members. When confidence was high, the “super stable” metrics remained “super stable,” but when low, none of the “super stable” metrics persisted as “super stable.” Our results suggest that nursing units represent what Barker (1968) termed dynamic behavior settings in which, as is typical, multiple nursing staff must constantly adjust to various circumstances, primarily through communication (e.g., discussing patient care or requesting advice on providing patient care), to preserve the functional integrity (i.e., ability to meet patient care goals) of the units, thus producing the observed stability over time of nine network metrics. The observed metric stability provides support for using network analysis to study communication patterns in dynamic behavior settings such as PCUs.     

Why does everybody hate me? Balance,status, and homophily: The triumvirate of signed tie formation

Despite being one of the foundational theories of signed (positive/negative) tie formation, the evidence for balance theory is far from conclusive. A recent promising alternative is status theory, but a theoretical and explanatory gap still remains, with a dearth of theories and evidence. We put forward and test eight separate theories of signed tie formation on two face-to-face networks of friendship and esteem of 282 students. We use dimension reduction (factor analysis) on the results tables comparing the predictions of these eight theories for 50 ERGM parameters with our estimated models. We find three main paradigms explain the majority of signed network formation: balance, status, and homophily.     

Multilevel meta network analysis with application to studying network dynamics of network interventions

In this paper, I introduce new methods for multilevel meta network analysis. The new methods can combine results from multiple network models, assess the effects of predictors at network or higher levels and account for both within- and cross-network correlations of the parameters in the network models. To demonstrate the new methods, I studied network dynamics of a smoking prevention intervention that was implemented in 76 classes of six middle schools in China. The results show that as compared to random intervention (i.e., that targets random students), smokers’ popularity was significantly reduced in the classes with network interventions (i.e., those target central students or students with their friends together). The findings highlight the importance of examining network outcomes in evaluating social and health interventions, the role of social selection in managing social influence, and the potential of using network methods to design more effective interventions.