Tag Archives: complex networks

CNETS PhD Program central in new $3 million NSF Training Grant

Luis RochaThe 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.

New Ph.D. Graduate

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.

Control of Complex Networks

Control of the eukaryotic cell cycle of budding yeast Saccharomyces cerevisiae (from Nature.com, click for details)

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.

Continue reading Control of Complex Networks

CNetS team studies generalized modularity in complex networks & Systems

mediumModularity 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.

CNetS team winner in LinkedIn Economic Graph Challenge

The CNetS team
The CNetS team

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…

New CASCI papers on Complex Networks

Network Science Journal
Network Science

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 [2015].”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 [2015]. “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 [2014]. “Multi-scale Integration and Predictability in Resting State Brain Activity”. Frontiers in Neuroinformatics, 8:66. doi: 10.3389/fninf.2014.00066.