Events
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Speaker
Magdalena Wrbel - 2010
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Speaker
Sergey BuldyrevDescription
Complex networks have been studied intensively for a decade, but research still focuses on the limited case of a single, non-interacting network. Modern systems are coupled together and therefore should be modeled as interdependent networks. A fundamental property of interdependent networks is that failure of nodes in one network may lead to failure of dependent nodes in other networks. This may happen recursively and can lead to a cascade of failures. In fact, a failure of a very small fraction of nodes in one network may lead to the complete fragmentation of a system of several interdependent networks. A dramatic real-world example of a cascade of failures ('concurrent malfunction') is the electrical blackout that affected much of Italy on 28 September 2003: the shutdown of power stations directly led to the failure of nodes in the Internet communication network, which in turn caused further breakdown of power stations20. Here we develop a framework for understanding the robustness of interacting networks subject to such cascading failures. We present exact analytical solutions for the critical fraction of nodes that, on removal, will lead to a failure cascade and to a complete fragmentation of two interdependent networks. Surprisingly, a broader degree distribution increases the vulnerability of interdependent networks to random failure, which is opposite to how a single network behaves. Our findings highlight the need to consider interdependent network properties in designing robust networks.
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Seminar: Slow Dynamics in aqueous systems at subzero temperatures: what can be learned by dielectric spectroscopy experiments?
Speaker
Simone CapaccioliDescription Broadband dielectric spectroscopy is a very powerful technique able to probe orientational dynamics of molecular dipoles over the timescale range 1 ps - 1 ks [1]. Therefore it is very convenient its application to water, that is characterized by a strong permanent molecular dipole moment. The dynamics of mixtures of water with various hydrophilic solutes can be probed over practically unrestricted temperature and frequency ranges, in contrast to bulk water where crystallization does not allow the study of the supercooled regime below 240 K. The characteristics of the dynamics of water and their trends observed in aqueous mixtures on varying the solutes and concentration of water will be shown. In particular, we will show the ubiquitous presence of a water related relaxation with the characteristics of a secondary beta-process, largely decoupled from the structural relaxation at low temperatures [2]. A comparison with the dynamics of nano-confined water, as obtained from dielectric spectroscopy measurements, will be also presented. All the results can help to infer the fundamental traits of the slow dynamics of water, among those we can mention the low degree of intermolecular coupling/cooperativity and the ‘strong’ character of the structural primary relaxation [3]. References [1] Udo Kaatze and Yuri Feldman, "Broadband dielectric spectrometry of liquids and biosystems" Meas. Sci. Technol. 17, R17–R35 (2006) [2] S. Capaccioli, K.L. Ngai and N. Shinyashiki, "The JG-beta relaxation of water" J. Phys. Chem. B 111, 8197 (2007) [3] K. L. Ngai, S. Capaccioli, S. Ancherbak, N. Shinyashiki, "Resolving the ambiguity of the dynamics of water and clarifying its role in hydrated proteins" Philosophical Magazine, (2010) DOI: 10.1080/14786435.2010.523716
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Seminar: Modules and statistical models of complex networks: from systems biology to the US Supreme Court
Speaker
Roger GuimeràDescription In complex systems, individual components interact with each other giving rise to complex networks, which are neither totally regular nor totally random. Because of the interplay between network topology and dynamics, it is crucial to characterize the structure of complex networks. The focus of most research on complex networks has been on global network properties. While global properties may sometimes provide useful insights, their relevance hinges strongly on the homogeneity of the networks. However, most real world networks display a marked modular structure, which means that, rather than being homogeneous in their connectivity, nodes tend to establish many more connections with a subset of the nodes in the network than with the remaining nodes. In my talk, I will discuss how we can use this modular structure to address two very prominent network problems: the problem of data reliability and network discovery, and the problem of extracting meaningful information from network data. I will illustrate the methods with examples from systems biology (metabolome and proteome) and from the social sciences (the voting patterns of US Supreme Court justices).
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Speaker
Aurelien DecelleDescription Detecting community structure from network topology is a well known problem with many possible applications. A large number of studies was conducted over the last decade, but a principal approach that would for instance output that a random graph does not have any community structure is still missing. Based on a random graph model for a community structure I will first show the existence of a phase transition between possible and impossible community inference. This phase transition is related to some known results from statistical physics of spin glasses, for optimal inference the partition function of a corresponding spin glass model needs to be computed. Then I will turn to real-world networks and inspired by the theoretical results I introduce a new message passing algorithm which is able to learn parameters of the community structure (number of communities, ...), and to infer the most likely community assignment. As an application I will present some results on real-world networks.