Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session X18: The Statistical Physics of Realworld Networks IIInvited

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Sponsoring Units: GSNP Chair: Guido Caldarelli, IMT Alti Studi Lucca Room: 205 
Friday, March 6, 2020 11:15AM  11:51AM 
X18.00001: The statistical physics of realworld networks: standing on Jaynes’ shoulders Invited Speaker: Fabio SARACCO

Friday, March 6, 2020 11:51AM  12:27PM 
X18.00002: Complex networks with complex nodes Invited Speaker: Raissa D'Souza The statistical physics perspective has provided a wealth of understanding about the structure and function of massive networks including phase transition behaviors, nontrivial network structures such as modularity and heterogeneous degree distributions, and the analysis of the dynamics unfolding on networks. It reveals the massive implications that network structure has on network function and resilience. Yet, complementary to this perspective of complex networks, simple networks of nonlinear nodes have been studied extensively in fields of dynamical systems and control theory. Real world networks  from brain networks to social networks to critical infrastructure networks  lie at the interface of both, with nonlinear nodes and highly nontrivial network structures. We are at a point in time when there is opportunity for these fields to come together. This talk will survey results of a recent project at three different scales on the complex node versus complex network spectrum, from synchronization in nanoscale oscillations to hierarchy and stability in multilayered social systems of macaque monkeys. 
Friday, March 6, 2020 12:27PM  1:03PM 
X18.00003: Percolation in real interdependent networks Invited Speaker: Filippo Radicchi The function of a real network depends not only on the reliability of its own components, but is affected also by the simultaneous operation of other real networks coupled with it. Robustness of systems composed of interdependent network layers is generally framed and characterized in terms of percolation models. In this talk, I will consider three different percolation models that provide different insights on the robustness of realworld interdependent networks. I will first consider the ordinary percolation model and illustrate a theoretical approach consisting in a system of heuristic equations able to approximate the phase diagram for arbitrary networks. Second, I will introduce and characterize the redundant percolation model, a genuine model for interdependent networks where the addition of new layers boosts system robustness by creating redundant interdependencies among network layers. Third, I will generalize the problem of optimal percolation from singlelayer to multilayer networks, and present several algorithms for finding approximate solutions to the problem. 
Friday, March 6, 2020 1:03PM  1:39PM 
X18.00004: Structure, phase transitions, and message passing in sparse networks Invited Speaker: Mark Newman Most networks and graphs encountered in empirical studies, including the Internet and the World Wide Web, social networks, and biological and ecological networks, are very sparse. Standard spectral and linear algebra methods perform poorly when applied to such networks. Message passing methods, such as belief propagation, offer an alternative that can deliver better performance as well as new analytic insights. This talk will introduce the message passing method through a progressive series of examples and illustrate how the method can be used for a wide range of calculations of network structure and function. Among other things, the talk will touch upon the calculation of percolation thresholds, graph spectra, and community structure, the deep connections between messagepassing fixed points and structural phase transitions in networks, and a new solution to the longstanding problem of message passing on networks with a high density of short loops. 
Friday, March 6, 2020 1:39PM  2:15PM 
X18.00005: A Networks View on Functional Brain Dynamics: timeseries, behavior and beyond Invited Speaker: Tommaso Gili Most of what we know about brain functioning comes from the time dependent investigation of its activity through the registration of specific proxies. Neurovascular and electrophysiological timeseries have been vastly used to associate the brain functional segregation with specific behavioral outcomes. In this presentation I will show the networks perspective of brain functioning by highlighting the stateoftheart of graph embedding in human neuroscience and the fundamental role of functional topology in supporting human behavior in healthy subjects and in neuropsychiatric patients. 
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