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.
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
Paper by Nir Grinberg, Kenneth Joseph, Lisa Friedland, Briony Swire-Thompson, and David Lazer
The spread of fake news on social media became a public concern in the United States after the 2016 presidential election. We examined exposure to and sharing of fake news by registered voters on Twitter and found that engagement with fake news sources was extremely concentrated. Only 1% of individuals accounted for 80% of fake news source exposures, and 0.1% accounted for nearly 80% of fake news sources shared. Individuals most likely to engage with fake news sources were conservative leaning, older, and highly engaged with political news. A cluster of fake news sources shared overlapping audiences on the extreme right, but for people across the political spectrum, most political news exposure still came from mainstream media outlets.
A recent article “Betweenness to assess leaders in criminal networks: New evidence using the dual projection approach” by Rosanna Grassi, Francesco Calderoni, and Monica Bianchi show the performance of different betweenness centrality measures in identifying criminal leaders in a meeting participation network. Each of the measures reports different ranking of leaders and dual projection based approaches show better performance compared to traditional betweenness or flow-based measures. Read more:
Complexity Explorables is a website where people easily explore some complex systems examples while playing models with fun.
For example, “I herd you!” enables you to explore how different network structures impact the spread of a disease in a population. Consequently, you can understand a phenomenon called “herd immunity”, defined that “a disease can be eradicated even if not the entire population is immunized.” The webpage is simple, yet very informative.
If you’re interested, there are many other examples and models. Check them out at http://www.complexity-explorables.org/explorables/!
An article “The weakness of tie strength” in the current issue of Social Networks unpacked three elements related to the strength of ties: capacity, frequency, and redundancy. The case with an email network shows that the three elements are not highly correlated and are likely to reflect different dimensions of ties. This multidimensional view may explain some unexpected empirical findings. For example, Garg and Telang (forthcoming in Management Science) found that strong ties in online social networks play a significant role in job search and weak ties are ineffective. Weak ties may generate some job information, but only strong ties lead to actions such as referrals.