A team of CNetS researchers has created the first global map of labor flow in collaboration with the world’s largest professional social network, LinkedIn. The work is reported in the journal Nature Communications. The study’s lead authors are Jaehyuk Park and Ian Wood, PhD students working with YY Ahn. Wood is currently a software engineer at LinkedIn. Other authors on the study are CNetS PhD student Elise Jing; Azadeh Nematzadeh of S&P Global, who contributed to the study as a CNetS PhD student; Souvik Ghosh of LinkedIn; and Michael Conover, a CNetS PhD graduate and senior data scientist at LinkedIn at the time of the study. CNetS researchers created the map using LinkedIn’s data on 500 million people between 1990 and 2015, including about 130 million job transitions between more than 4 million companies. The researchers gained access to this data as one of only two teams — IU and MIT — selected to continue their work on the LinkedIn Economic Graph Research program beyond 2017. The study’s result represents a powerful tool for understanding the flow of people between industries and regions in the U.S. and beyond. It could also help policymakers better understand how to address critical skill gaps in the labor market or connect workers with new opportunities in nearby communities. More…
The National Institutes of Health, under the National Library of Medicine’s program on data science research, awarded a $1.55 million grant to an interdisciplinary team lead by Luis Rocha, a professor of informatics, member of CNETS and the director of the NSF-NRT complex networks & systems program at the School of Informatics, Computing, and Engineering. The four-year project, a collaboration between SICE and the Indiana University School of Nursing, will employ innovative data- and network-science methods to produce myAURA, an easy-to-use web service for epilepsy patients. myAURA will be based on a large-scale epilepsy knowledge graph built by integrating data from social media, electronic health records, patient discussion boards, scientific literature databases, advocacy websites, and mobile app data. The knowledge graph will, in turn, be used to fuel recommendation and visualization algorithms based on the automatic inference of relevant associations. The inference will follow algorithms developed by Rocha’s team to remove redundancy and extract factual information from large knowledge graphs as well as parsimonious network visualizations developed by Katy Börner, Distinguished Professor of Engineering & Information Science at SICE. Continue reading CNetS team awarded NIH grant to improve chronic-disease management with Data and Network Science
First global analysis of human birth-rate cycles reveals that post-holiday ‘baby boom’ persists across cultures, hemispheres. CNetS PhD student Ian Wood and Professors Luis Rocha and Johan Bollen, in collaboration with Joana Sá, used data science and computational social science methods to demonstrate that “Human Sexual Cycles are Driven by Culture and Match Collective Moods.” See full article at IU News and media coverage in many venues such as The Independent, Time, Newsweek, Publico, ScienceDaily, Phys.org, The National Post, DailyMail, The Hindustan Times, Men’s Fitness, Mother Jones, Drive with Yasmeen Khan (at 17:30) (audio of interview), etc. Discussion of the paper was a top trending topic on Reddit. Watch a short video about the research.
The National Science Foundation has awarded nearly $3 million to train future research leaders in Complex Networks and Systems, via the PhD Program established by CNETS faculty. The highly selective grant from the NSF’s Research Traineeship Award will create a dual Ph.D. program at Indiana University to train graduate students to be proficient in both a specific discipline, such as psychology or political science, as well as network, complexity and data science. The new Ph.D. program will also leverage the strengths of the Indiana Network Science Institute, or IUNI, to involve students in interdisciplinary research.”The biggest challenges currently faced by society require large teams of people who are ‘fluent’ in more than one scientific discipline,” said Luis Rocha, CNETS professor in the IU School of Informatics, Computing, and Engineering who will lead the new program. “But the current education model in academia is still largely focused on training researchers who know how to set up independent labs with agendas driven by a single person. If we want to take on the really big problems, we’ve got to create more scientists with deep expertise in multiple areas.” Full Press Release Available.
Congratulations to Onur Varol for successfully defending his dissertation entitled “Analyzing Social Big Data to Study Online Discourse and its Manipulation” on April 25th 2017, supervised by Filippo Menczer. Onur completed a PhD degree in the Complex Systems track of the Informatics PhD Program. Onur has accepted a postdoctoral position at Northeastern University at the Center for Complex Network Research.
Congratulations to Santosh Manicka for successfully defending his dissertation entitled “The Role of Canalization in the Spreading of Perturbations in Boolean Networks” on April 24th 2017, Supervised by Luis Rocha. Santosh completed a PhD degree in the Complex Systems track of the Informatics PhD Program.
Congratulations to Alexander Gates for successfully defending his dissertation entitled “The anatomical and effective structure of complex systems” on April 3rd 2017, co-supervised by Randy beer and Luis Rocha. Alex completed a dual-PhD degree in the Complex Systems track of the Informatics PhD Program as well as the Cognitive Science program at Indiana University. Alex has accepted a postdoctoral position at Northeastern University at the Center for Complex Network Research.
Network science has allowed us to understand the organization of complex systems across disciplines. However, there is a need to understand how to control them; for example, to identify strategies to revert a diseased cell to a healthy state in cancer treatment. Recent work in the field—based on linear control theory—suggests that the controllability of complex systems can be predicted solely from the graph of interactions between variables, without considering their dynamics. Such graph-based approaches have been used, for instance, to suggest that biological systems are harder to control and have appreciably different control profiles than social or technological systems. The methodology has also been increasingly used in many applications from financial to biochemical networks.
In work published today in Nature Scientific Reports, CNetS graduate student Alexander Gates and Professor Luis Rocha demonstrate that such graph-based methods fail to characterize controllability when dynamics are introduced. The study computed the control profiles of large ensembles of multivariate systems as well as existing Systems Biology models of biochemical regulation in various organisms.
The Center for Complex Networks and Systems Research (CNetS.indiana.edu), jointly with the Indiana University Network Science Institute (IUNI.iu.edu), has
two three open postdoctoral positions, two on the characterization and modeling of complex systems and one to study critical processes in networks of networks. The appointments start in Summer/Fall 2016 for one year and are renewable for one or two additional years, subject to funding and performance. The salary is competitive and benefits are generous.
The postdocs will join a dynamic and interdisciplinary team that includes computer, physical, and cognitive scientists. Two postdocs will work with Prof. Santo Fortunato on various areas of complex systems research, including community detection in networks, computational social science (opinion dynamics, online experiments on social influence) and science of science (citation and collaboration patterns between scientists, impact dynamics). A third postdoc will work with Prof. Filippo Radicchi. Continue reading Three postdoc positions in complex networks and systems
Modularity in complex systems can be observed in networks and across dynamical states, time scales, and in response to different kinds of perturbations. In a paper published in Physical Review E (Rapid Communication), Kolchinsky, Gates & Rocha propose a principled alternative to detecting communities in static and dynamical networks. The method demonstrates that standard modularity measures on static networks can be seen as a special case of measuring the spread of perturbations in dynamical systems. Thus, the new method offers a powerful tool for exploring the modular organization of complex dynamical systems.