CNetS students, postdocs, and faculty members will give 7 regular talks and present 13 posters at NetSci 2020, held online this year due to COVID-19. Regular talks will cover research on many topics including COVID-19, forecasting social contagion of anti-vax ideas, political bias in social media, coordinated manipulation online, the scientific development of nations, hierarchy in faculty hiring networks, and citation cartels in journals.Continue reading CNetS @ NetSci 2020
The book A First Course in Network Science by CNetS faculty members Filippo Menczer and Santo Fortunato and CNetS PhD graduate Clayton A. Davis was recently published by Cambridge University Press. This textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Extensive tutorials, datasets, and homework problems provide plenty of hands-on practice. The book has been endorsed as “Rigorous” (Alessandro Vespignani), “comprehensive… indispensable” (Olaf Sporns), “with remarkable clarity and insight” (Brian Uzzi), “accessible” (Albert-László Barabási), “amazing… extraordinary” (Alex Arenas), and “sophisticated yet introductory… an excellent introduction that is also eminently practical” (Stephen Borgatti). It was ranked by Amazon #1 among new releases in physics. More…
The outcome of the DREAM Challenge on Disease Module Identification in genetic networks has been reported in a paper published in Nature Methods. Over 400 participants from all around the world have contributed 75 different clustering algorithms to predict disease-relevant modules in diverse gene and protein networks. Participants could only use unsupervised clustering algorithms, which rely exclusively on the network structure and do not depend on additional biological information such as known disease genes. CNetS professor Santo Fortunato and former postdoc Lucas Jeub participated in the analysis of the results delivered by the algorithms.Continue reading DREAM Challenge paper published in Nature Methods
Two CNetS teams were awarded prestigious awards from Minerva, a research initiative of the Department of Defense that supports basic social science research focusing on topics of particular relevance to U.S. national security. One of the two awards will develop Science Genome, a new quantitative framework to investigate science of science using representation learning and graph embedding. The $4.4M project will take advantage of the availability of digitized bibliographic data sets and powerful computational methods, such as machine learning with deep neural networks, to tap into hidden information present in complex scholarly graphs. The project is led by YY Ahn and also includes Staša Milojević, Alessandro Flammini, and Fil Menczer (more…). The other award aims to understand the fundamental laws ruling science dynamics: the description and prediction of the evolution of scientific fields, how to define and measure the novelty of a scientific work, how to assemble successful teams to solve a specific task, and how to define and measure the impact of scholars’ research. The $5M project is led by a consortium of seven prominent science of science experts in four US institutions, including CNetS professor Santo Fortunato (more…). Both projects have potential applications in policy-making, for institutions and funding agencies.
A new paper published in Nature Reviews Physics by Professor Santo Fortunato and colleagues from Northwestern University features a detailed analysis of the careers of Nobel Prize Laureates. They found that the prize- winning works in the three main science categories (physics, chemistry and medicine) tend to occur early in the career of the Laureate.
This may be due to a selection effect — because the Nobel Prize in science has never been awarded posthumously, those who produced groundbreaking works early on in their careers were more likely to wait long enough to be recognized. Also, award-winning papers tend to be produced by small teams, on average. Apart from the prize-winning work, which may be subject to peculiarities of the Nobel, there is no known major difference that distinguishes patterns governing the careers of scientific elites from those of ordinary scientists.