Anomalous Contagion and Renormalization in Dynamical Networks with Nodal Mobility

The common real-world feature of individuals migrating through a network -- either in real space or online -- significantly complicates understanding of network processes. Here we show that even though a network may appear static on average, underlying nodal mobility can dramatically distort outbreak profiles. Highly nonlinear dynamical regimes emerge in which increasing mobility either amplifies or suppresses outbreak severity. Predicted profiles mimic recent outbreaks of real-space contagion (social unrest) and online contagion (pro-ISIS support). We show that this nodal mobility can be renormalized in a precise way for a particular class of dynamical networks

A computational science approach to understanding human conflict

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Adaptive link dynamics drive online hate networks and their mainstream influence

Online hate is dynamic, adaptive -- and is now surging armed with AI/GPT tools. Its consequences include personal traumas, child sex abuse and violent mass attacks. Overcoming it will require knowing how it operates at scale. Here we present this missing science and show that it contradicts current thinking. Waves of adaptive links connect the hate user base over time across a sea of smaller platforms, allowing hate networks to steadily strengthen, bypass mitigations, and increase their direct influence on the massive neighboring mainstream. The data suggests 1 in 10 of the global population have recently been exposed, including children. We provide governing dynamical equations derived from first principles. A tipping-point condition predicts more frequent future surges in content transmission. Using the U.S. Capitol attack and a 2023 mass shooting as illustrations, we show our findings provide abiding insights and quantitative predictions down to the hourly scale. The expected impacts of proposed mitigations can now be reliably predicted for the first time

Shockwaves and turbulence across social media

Online communities featuring 'anti-X' hate and extremism, somehow thrive online despite moderator pressure. We present a first-principles theory of their dynamics, which accounts for the fact that the online population comprises diverse individuals and evolves in time. The resulting equation represents a novel generalization of nonlinear fluid physics and explains the observed behavior across scales. Its shockwave-like solutions explain how, why and when such activity rises from 'out-of-nowhere', and show how it can be delayed, re-shaped and even prevented by adjusting the online collective chemistry. This theory and findings should also be applicable to anti-X activity in next-generation ecosystems featuring blockchain platforms and Metaverses.Comment: Feedback welcome to [email protected]

Extremes in Complex Systems

This dissertation focuses on extreme behavior in complex systems. Extreme events are an emergent property of many complex, nonlinear systems in which various interdependent components and their interaction lead to a competition between organized (interaction dominated) and irregular (fluctuation dominated) behavior. In this work we investigated several aspects of extreme events in pro-ISIS online social network system and the subsecond financial exchange market. We started by uncovering an ultrafast ecology driving the online support, featuring self-organized aggregates whose evolution exhibits novel adaptive mechanisms in response to external pressure. Then we investigated the anomalous contagion, we provided and analyzed a simple, yet highly non-trivial, model in which the processes of human mobility and infection dynamics co-exist with competing timescales, we showed that even in this static steady-state limit, a non-zero nodal mobility leads to a diverse set of outbreak profiles that is dramatically different from known forms, and yet matches well with recent real-world social outbreaks. In the quest to understand dynamical network mechanisms underlying aging of an online organism from birth to death, we present the continuous-time evolution of an online organism network from birth to death which crosses all organizational and temporal scales, from individual components through to the mesoscopic and entire system scale, we analyzed the body of pro-ISIS support that developed organically on VKontkate , and which made VKontakte a dominant social media site for ISIS recruitment, propaganda and financing. Our continuous-time study of its entire life cycle from initial growth (late 2014) until eventual death in late 2015, complements and extends existing landmark studies of dynamical networks. We also present a generalized gelation theory that describes human online aggregation in support of extremism. The theory that we develop shows that heterogeneous systems with aggregation rules based on objects' mutual affinity, can effectively delay the gel transition point and drastically alter its growth rate. We show that the theory provides an accurate description of the online extremist support for ISIS which started in late 2014. The last part of this dissertation, we focused on the important problem of how delayed information in such subsecond systems impacts their overall stability.</p

Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjects

Abstract Longitudinal deep multiomics profiling, which combines biomolecular, physiological, environmental and clinical measures data, shows great promise for precision health. However, integrating and understanding the complexity of such data remains a big challenge. Here we utilize an individual-focused bottom-up approach aimed at first assessing single individuals’ multiomics time series, and using the individual-level responses to assess multi-individual grouping based directly on similarity of their longitudinal deep multiomics profiles. We used this individual-focused approach to analyze profiles from a study profiling longitudinal responses in type 2 diabetes mellitus. After generating periodograms for individual subject omics signals, we constructed within-person omics networks and analyzed personal-level immune changes. The results identified both individual-level responses to immune perturbation, and the clusters of individuals that have similar behaviors in immune response and which were associated to measures of their diabetic status

Stellar Angle-Aided Pulse Phase Estimation and Its Navigation Application

X-ray pulsar-based navigation (XNAV) is a promising autonomous navigation method, and the pulse phase is the basic measurement of XNAV. However, the current methods for estimating the pulse phase for orbiting spacecraft have a high computational cost. This paper proposes a stellar angle measurement-aided pulse phase estimation method for high Earth orbit (HEO) spacecraft, with the aim of reducing the computational cost of pulse phase estimation in XNAV. In this pulse phase estimation method, the effect caused by the orbital motion of the spacecraft is roughly removed by stellar angle measurement. Furthermore, a deeply integrated navigation method using the X-ray pulsar and the stellar angle is proposed. The performances of the stellar angle measurement-aided pulse phase estimation method and the integrated navigation method were verified by simulation. The simulation results show that the proposed pulse phase estimation method can handle the signals of millisecond pulsars and achieve pulse phase estimation with lower computational cost than the current methods. In addition, for HEO spacecraft, the position error of the proposed integrated navigation method is lower than that of the stellar angle navigation method