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.
Our understanding of how catastrophe propagates in multi-layered networks relies on theories that apply only to infinite systems. As a paper published in Nature Physics by Filippo Radicchi demonstrates, reducing an interconnected network of finite size to a multiset of decoupled graphs provides a route to understanding catastrophic events in real systems.
LinkedIn announced that YY Ahn and his team of Ph.D. students from the Center for Complex Networks and Systems Research, including Yizhi Jing, Adazeh Nematzadeh, Jaehyuk Park, and Ian Wood, is one of the 11 winners of the LinkedIn Economic Graph Challenge.
Their project, “Forecasting large-scale industrial evolution,” aims to understand the macro-evolution of industries to track businesses and emerging skills. This data would be used to forecast economic trends and guide professionals toward promising career paths.
“This is a fascinating opportunity to study the network of industries and people with unprecedented details and size. All of us are very excited to collaborate with LinkedIn and our LinkedIn mentor, Mike Conover, who is a recent Informatics PhD alumnus, on this topic,” said Ahn. Read more…
Read new papers from CASCI on developing the mathematical toolbox available to deal with computing distances on weighted graphs, applying distance closures for computational fact checking, and computing multi-scale integration in brain networks:
T. Simas and L.M. Rocha .”Distance Closures on Complex Networks”. Network Science, doi:10.1017/nws.2015.11.
G.L. Ciampaglia, P. Shiralkar, L.M. Rocha, J. Bollen, F. Menczer, A. Flammini . “Computational fact checking from knowledge networks.” PLoS One. In Press. arXiv:1501.03471.
A. Kolchinsky, M. P. Van Den Heuvel, A. Griffa, P. Hagmann, L.M. Rocha, O. Sporns, J. Goni . “Multi-scale Integration and Predictability in Resting State Brain Activity”. Frontiers in Neuroinformatics, 8:66. doi: 10.3389/fninf.2014.00066.
The new Indiana University Network Science Institute (IUNI) unites 100+ researchers at IU — building on their world-renowned multidisciplinary expertise toward further scientific understanding of the complex networked systems of our world. Through pioneering new approaches in mapping, representing, visualizing, modeling, and analyzing diverse complex networks across levels and disciplines, IUNI will lead the way. We keep track of the big picture — ever-changing and interconnected. We’re laying the groundwork for innovative research and discovery in the area of network science.