Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session K42: Network Theory and Applications to Complex SystemsInvited Live Streamed
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Sponsoring Units: GSNP Chair: Filippo Radicchi, Indiana University Room: McCormick Place W-375A |
Tuesday, March 15, 2022 3:00PM - 3:36PM |
K42.00001: Towards multiscale network science Invited Speaker: M. Angeles Serrano Network geometry offers a powerful framework for solving network problems which have important features at multiple length scales. Within this paradigm, we have been developing methods for exploring real networks at different resolutions on the basis of similarity distances between nodes in a latent hyperbolic space. The core method is a geometric renormalization technique that coarse-grains and rescales the network to unfold it into a multilayer shell. We found that the multiscale shells of real networks, including connectomes of the human brain, show self-similarity, which is also found in the evolution of some growing real networks. This suggests that evolutionary processes can be modeled by a reverse renormalization process, meaning that the same principles that shape connectivity in networks at different length scales also remain over time. Multiscale unfolding has also practical applications. For instance, it can be used to produce scaled down and scaled up replicas of real networks, a useful tool for the study of processes where network size is relevant, or in the optimization of dynamical processes. |
Tuesday, March 15, 2022 3:36PM - 4:12PM |
K42.00002: Complex Contagion: Unfolding and Control Invited Speaker: Adilson E Motter Network cascades are a pervasive phenomenon, with examples ranging from supply chain disruptions, traffic congestions, power outages, and default contagion in financial networks to the spread of misinformation. Such cascades propagate through underlying networks, but in contrast with their epidemic counterparts, they constitute a form of "complex contagion," in the sense that the "infection" of a new component may require exposure to multiple infected components. Compared to other network spreading processes, cascades may be harder to trigger but also harder to mitigate. In this talk, I will discuss unique ways in which cascades evade mitigation and then present effective mitigation strategies that avoid evasion. The conclusions are relevant in face of the plethora of ripple effects caused by the ongoing pandemic on a multitude of local and global networks. |
Tuesday, March 15, 2022 4:12PM - 4:48PM |
K42.00003: The Statistical Physics of Financial Networks Invited Speaker: Guido Caldarelli As the total value of the global financial market outgrew the value of the real economy, financial institutions created a global web of interactions that embodies systemic risks. Understanding these networks requires new theoretical approaches and new tools for quantitative analysis. Statistical physics contributed significantly to this challenge by developing new metrics and models for the study of financial network structure, dynamics, and stability and instability. In this Review, we introduce network representations originating from different financial relationships, including direct interactions such as loans, similarities such as co-ownership and higher-order relations such as contracts involving several parties (for example, credit default swaps) or multilayer connections (possibly extending to the real economy). We then review models of financial contagion capturing the diffusion and impact of shocks across each of these systems. We also discuss different notions of 'equilibrium' in economics and statistical physics, and how they lead to maximum entropy ensembles of graphs, providing tools for financial network inference and the identification of early-warning signals of system-wide instabilitie |
Tuesday, March 15, 2022 4:48PM - 5:24PM |
K42.00004: Network inference and the detectability of closed-form mathematical models from data Invited Speaker: Roger Guimera For a few centuries, scientists have described natural phenomena by means of relatively simple mathematical models such as Newton's law of gravitation or Snell's law of refraction. Sometimes, they found these models deductively, starting from fundamental considerations; more frequently, however, they derived the models inductively from data. With increasing amounts of data available for all sorts of (natural and social) systems, one may argue that we are now in a position to inductively uncover new interpretable models for these systems. But can this process be authomatized? That is, can we design algorithms that automatically learn, from data, the closed-form mathematical models that generated them? And if so, are the true generating models always learnable? Here we will discuss how network inference approaches can help us to answer these questions. Moreover, we will show that there is a transition occurring between: (i) a learnable phase at low observation noise, in which the true model can in principle be learned from the data; and (ii) an unlearnable phase, in which the observation noise is too large for the true model to be learned from the data by any method. |
Tuesday, March 15, 2022 5:24PM - 6:00PM |
K42.00005: TBD Invited Speaker: Alessandro Vespignani
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