Santo Fortunato has been elected Fellow of the Network Science Society (NSS) for seminal work in network community structure leading to advances in multiresolution approaches and validation, and for contributions to disseminating network science. The Fellowship is awarded annually to members of the community for their exceptional lifelong individual contributions to any area of network science research and to the community of network scientists, both locally and globally. The award has been announced at NetSci 2022, the flagship event of the NSS. Fortunato is the first member of Luddy to receive this prestigious recognition. Check the official press release of the school!
We have two big announcements! First, CNetS (along with IUNI and OSoMe) is moving to the new Luddy Center for Artificial Intelligence. Second, we have a new tenure-track assistant professor position in Artificial Intelligence and Network Science. We welcome any candidates who study AI, complex systems, and network science (all broadly defined). Potential research areas include, but are not limited to, deep learning, graph neural networks, complex systems, complex networks, computational neuroscience, computational social science, social media analytics, agent-based models, and the impacts of AI and social media on society. We especially welcome applications from members of underrepresented groups in computing. More info and application here!
The Army Research Office has awarded the grant Multilayer network embeddings and applications to real-world problems to CNetS faculty Santo Fortunato and Filippo Radicchi. The project lies at the interface between artificial intelligence and network science and aims at developing embeddings of multilayer networks in vector space. While graph embeddings have become very popular over the past decade, most of the research in this area focuses on the analysis of isolated graphs. However, networks in the real world do not exist in isolation, but they are coupled with other networks. For example in social media, the same person may interact with different individuals depending on the online platform.
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.
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.
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.