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).
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).
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 works 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.
This work has leveraged data from the Wikimedia project, as well as from Mark Meiss, concerning traffic to Wikipedia pages and pages in the Internet at large.
NaN is a research group exploring the modeling, simulation, and analysis of complex social and information networks, adaptive agents, and social computing systems. We especially focus on social media and the Web as complex techno-social networks in which we leave abundant traces of our activities: what we do, what we are interested in, whom we talk to, what knowledge we acquire and contribute. Our research spans from modeling the dynamic processes that occur online (how information networks grow and evolve, how individual and collective traffic patterns emerge, how attention bursts are generated and shaped by social and search tools) to designing tools that mine the Web to build better search, navigation, management, and recommendation tools (where ‘better’ means more intelligent, autonomous, robust, personalized, contextual, scalable, adaptive, and so on). We have ongoing collaborations with colleagues at the ISI Foundation, Yahoo Labs, and MoBS Lab.