Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session S53: 20 Years of Scaling and Dynamics in Complex NetworksInvited
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Sponsoring Units: GSNP Chair: Alessandro Vespignani, Northeastern Univ Room: BCEC 253C |
Thursday, March 7, 2019 11:15AM - 11:51AM |
S53.00001: Twenty Years of Network Science: From Structure to Control Invited Speaker: Albert Barabasi Systems as diverse as the cell, the brain, the World Wide Web, or the social systems are described by highly interconnected networks with complex topology, whose structure determines their function and utility. Twenty years a research has shown that these networks are the result of self-organizing processes governed by simple but generic laws, that can be best understood using tools rooted in statistical physics. These studies have also offered evidence of a deep universality, finding that many real networks share multiple common architectural features, from the scale-free property, discovered 20 year ago today, to communities and correlations. I will discuss the order characterizing real networks and its implication, with focus on network control. Indeed, most complex systems have purpose, striving to accomplish some function, from the cell’s ability to reproduce to the brain’s ability to control our motions. For this, the underlying networks must be wired to be able to constantly control the system’s internal processes, in response to external inputs and perturbations. I will show how to adapt the tools of control theory to unveil the control principles of complex self-organized systems. Finally, I will discuss a recently developed analytical framework to study the controllability of an arbitrary complex network, and offer experimental evidence for its direct applicability to neural networks in the brain. |
Thursday, March 7, 2019 11:51AM - 12:27PM |
S53.00002: Multilayer Networks: Structure and Dynamics Invited Speaker: Ginestra Bianconi Multilayer networks are emerging as a novel and powerful way to describe complex systems. Multilayer networks are ubiquitous and include social networks, financial markets, infrastructures, molecular networks and the brain. |
Thursday, March 7, 2019 12:27PM - 1:03PM |
S53.00003: Temporal networks Invited Speaker: Petter Holme The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions—modeling your system as temporal networks—can make predictions and mechanistic understanding more accurate. Just as there can be network structures affecting disease spreading, temporal structures can also govern the spreading dynamics. We will discuss recent developments in the analysis of temporal networks, including community detection, the definition of time scales, random walks and various forms of spreading processes. We argue that adding time to network representations fundamentally changes our usual network concepts—so much that it is perhaps meaningless to think of temporal networks as an extension of the network paradigm. |
Thursday, March 7, 2019 1:03PM - 1:39PM |
S53.00004: Epidemic threshold on temporal networks Invited Speaker: Vittoria Colizza Our understanding of communicable diseases prevention and control is rooted in the theory of host population transmission dynamics. The network of host-to-host contacts along which transmission can occur drives the epidemiology of communicable diseases, determining how quickly they spread and who gets infected. A large body of epidemiological, mathematical and computational studies has provided a number of insights into the understanding of the process and the identification of efficient control strategies. The explosion of time resolved contact data has however opened the stage to new challenges. What are the structural and temporal aspects, and possibly their non-trivial interplay, that are critical for disease spread? To answer this question, I will introduce the infection propagator approach, a theoretical framework for the assessment of the degree of vulnerability of a host population to disease epidemics, once we account for the time variation of its contact pattern. By reinterpreting the tensor formalism of multilayer networks, this approach allows the analytical computation of the epidemic threshold for an arbitrary time-varying network of host contacts, i.e. the critical pathogen transmissibility above which large-scale propagation occurs. I will apply this framework to a set of empirical time-varying contact networks and show how it can be used to test different intervention strategies for infection prevention and control in realistic settings. |
Thursday, March 7, 2019 1:39PM - 2:15PM |
S53.00005: Converse Symmetry Breaking Invited Speaker: Adilson Motter Symmetry breaking--the phenomenon in which the symmetry of a system is not inherited by its stable states--underlies pattern formation, superconductivity, and numerous other effects. In this talk, I will report on the recently established possibility of converse symmetry breaking, an emergent network phenomenon in which the stable states are symmetric only when the system itself is not. In particular, I will present an experimental demonstration of this phenomenon as well as concrete applications to network optimization and control. The presentation will also discuss how converse symmetry breaking challenges the fundamental and widely held assumption that identical agents are necessarily more likely to exhibit similar behavior. I will show that it can, in fact, give rise to beneficial effects of heterogeneity in numerous complex systems in which interacting entities are required to exhibit coordinated behavior. Through this presentation, I hope to convey that our research in network science is now not only benefiting from statistical and nonlinear physics, but also fostering foundational discoveries in these areas. |
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