A research paper by Park et al. 

While there are existed studies on geo-industrial clusters, researchers have been lacking extensive empirical data to capture the organic and emergent nature of clusters and their dynamics for the global economic context. To address this research gap, recently, researchers in Indiana University and LinkedIn constructed a global labor flow network to examine the organization and evolvement of economies. Using a rich LinkedIn’s employment history data set, they focused on the flow (job transitions of workers) between firms, industries, and regions. Their results shed light on a new systematic approach to identify geo-industrial clusters and informed future economic analysis.

Read the paper here: https://www.nature.com/articles/s41467-019-11380-w

A recent article by Daniel Sewell develops a new approach for Cognitive Social Structures (CSS) data. Although there have been several models for CSS, his latent space models better capture micro-structures of CSS and provide insights into respondents’ perceptions.

If you’re interested in the paper, it’s available: https://www.cambridge.org/core/journals/network-science/article/latent-space-models-for-network-perception-data/0D24F6D47F4FF81EE19AAC083DADEB35.

A recent American Journal of Sociology article by Georg Rilinger develops a relational theory of complex secrets which explains how corporate crimes often remain secrets even after the fact that critical information has been revealed. The author argues that this type of secrets (i.e., complex secrets) is not enough to be identified secrets as “things,” compared to simple secrets (i.e., discovering a fact reveals a secret). Rather, it requires those who discover secrets to (a) find whole sets of information and then (b) assemble them properly based on a guiding conception. The author demonstrated the case of complex secrets using the Insull’s Ponzi scheme in the 1920s and 1930s. In particular, in this scandal, there were four FTC investigations and early ones failed. The author illustrated that despite the fact that all the investigations had the same sets of information, the early ones relied on a misguided conception, which prevented them from successfully discovering the complex secrets.

If you’re interested in the article, go visit: https://www.journals.uchicago.edu/doi/abs/10.1086/702730

A research article by Lingfei Wu, Dashun Wang, and James A. Evans.

One of the most universal trends in science and technology today is the growth of large teams in all areas, as solitary researchers and small teams diminish in prevalence. Increases in team size have been attributed to the specialization of scientific activities3, improvements in communication technology, or the complexity of modern problems that require interdisciplinary solutions. This shift in team size raises the question of whether and how the character of the science and technology produced by large teams differs from that of small teams. Here we analyse more than 65 million papers, patents and software products that span the period 1954–2014, and demonstrate that across this period smaller teams have tended to disrupt science and technology with new ideas and opportunities, whereas larger teams have tended to develop existing ones. Work from larger teams builds on more-recent and popular developments, and attention to their work comes immediately. By contrast, contributions by smaller teams search more deeply into the past, are viewed as disruptive to science and technology and succeed further into the future—if at all. Observed differences between small and large teams are magnified for higher-impact work, with small teams known for disruptive work and large teams for developing work. Differences in topic and research design account for a small part of the relationship between team size and disruption; most of the effect occurs at the level of the individual, as people move between smaller and larger teams. These results demonstrate that both small and large teams are essential to a flourishing ecology of science and technology, and suggest that, to achieve this, science policies should aim to support a diversity of team sizes.

Link: https://www.nature.com/articles/s41586-019-0941-9

A new study by the Human Nature Lab at Yale University explored how people allocate a limited, but personally usable, resource (e.g., unused Wi-Fi bandwidth) to their neighbors. Based on results from a Wi-Fi sharing game that the authors developed, the study found that (a) network density (i.e., the extent which people are connected with each other in the network) impacts the inequality of Wi-Fi sharing, and (b) those who benefit from Wi-Fi sharing at most tend to have many neighbors who in turn have few neighbors.

If you’re interested in the study, it is available: https://www.nature.com/articles/s41467-019-08935-2