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.
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.
The CNetS poster “The Rise of Social Bots in Online Social Networks” by Emilio Ferrara, Onur Varol, Prashant Shiralkar, Clayton Davis, Filippo Menczer, and Alessandro Flammini won a Best Poster Award at CCS 2015. The poster was presented by Clayton Davis. The results will also appear in the paper “The Rise of Social Bots” to be published in Comm. ACM (in press, preprint).
The paper “Modularity and the Spread of Perturbations in Complex Dynamical Systems” by Artemy Kolchinsky, Alexander J. Gates and Luis M. Rocha, and the poster “Information Theoretic Structures of the French Revolution” by Alexander Barron, Simon DeDeo and Rebecca Spang won additional awards.
Finally, our former postdoctoral scientist Bruno Gonçalves (now tenured faculty member at Aix-Marseille Université) received a Junior Scientist Award from the Complex Systems Society for his contributions to the study of human social behavior from large-scale online attention and behavioral data. This is the second Junior Scientist Award for CNetS (the first was won by Filippo Radicchi).
Congratulations to the CNetS team!
Online popularity can be thought of as analogous to an earthquake; it is sudden, unpredictable, and the effects are severe. While shifts in online popularity are not inherently destructive – consider the unprecedented magnitude of online giving via Twitter following the disaster in Haiti – they indicate radical swings in society’s collective attention. Given the increasingly profound effect that large-scale opinion formation has on important phenomena like public policy, culture, and advertising profits, understanding this behavior is essential to understanding how the world operates.
In this paper by Ratkiewicz and colleagues, the authors put forth a web-wide analysis that includes large-scale data sets of the online behaviors of millions of people. The paper offers a novel model that is is capable of reproducing all of the observed dynamics of online popularity through a mechanism that causes sudden, nonlinear bursts of collective attention. These results have been mentioned in the APS and PhysOrg websites.
The Web Dynamics group worked to build a better understanding of how the Web, the Wikipedia, and similar large information networks, grow and change over their lifetime. Of particular interest is how nodes in these networks gain popularity.
Our work painted a picture of the Web as a place in which popularity is very dynamic and unpredictable. Surges in popularity for topics are similar to earthquakes and avalanches in terms of their unpredictability — both in when they will happen and on what scale. However, we found that spikes in popularity are often correlated with events in the news — as evidenced by positive correlation between Google Trends data and traffic to bursty Wikipedia topics. Work on this project has been presented at SocialCom 2010 Symposium on Social Intelligence and Networking (SIN-10). A review of these issues with an emphasis on the modeling problem was also published in Physical Review Letters.
Finally, we studied the production of information in the attention economy — namely, how the production of new knowledge is associated to significant shifts of collective attention, which we take as proxy for its demand. This is consistent with a scenario in which allocation of attention toward a topic stimulates the demand for information about it, and in turn the supply of further novel information.
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Networks & agents Network (NaN)
NaN is a research group exploring the modeling, simulation, and analysis of complex social and information networks, and the human and artificial agents who live in these networks. Broadly speaking our research spans network science, data science, web science, and computational social science. Recently our focus has been on modeling the dynamic processes that occur online (how information networks grow and evolve, how memes go viral, how social media can be manipulated for the spread of misinformation, how attention bursts and other traffic patterns emerge, etc.) and on the design of tools to make the Web and social media ‘better’ (more trustworthy, reliable, intelligent, autonomous, robust, personalized, contextual, scalable, adaptive, and so on). We collaborate with colleagues at the IU Network Science Institute (IUNI), ISI Foundation, Yahoo Research, and many other institutions.