Protection against Contagion in Complex Networks

In real-world complex networks, harmful spreads, commonly known as contagions, are common and can potentially lead to catastrophic events if uncontrolled. Some examples include pandemics, network attacks on crucial infrastructure systems, and the propagation of misinformation or radical ideas. Thus, it is critical to study the protective measures that inhibit or eliminate contagion in these networks. This is known as the network protection problem. The network protection problem investigates the most efficient graph manipulations (e.g., node and/or edge removal or addition) to protect a certain set of nodes known as critical nodes. There are two types of critical nodes: (1) predefined, based on their importance to the functionality of the network; (2) unknown, whose importance depends on their location in the network structure. For both of these groups and with no assumption on the contagion dynamics, I address three major shortcomings in the current network protection research: namely, scalability, imprecise evaluation metric, and assumption on global graph knowledge. First, to address the scalability issue, I show that local community information affects contagion paths through characteristic path length. The relationship between the two suggests that, instead of global network manipulations, we can disrupt the contagion paths by manipulating the local community of critical nodes. Next, I study network protection of predefined critical nodes against targeted contagion attacks with access to partial network information only. I propose the CoVerD protection algorithm that is fast and successfully increases the attacker’s effort for reaching the target nodes by 3 to 10 times compared to the next best-performing benchmark. Finally, I study the more sophisticated problem of protecting unknown critical nodes in the context of biological contagions, with partial and no knowledge of network structure. In the presence of partial network information, I show that strategies based on immediate neighborhood information give the best trade-off between performance and cost. In the presence of no network information, I propose a dynamic algorithm, ComMit, that works within a limited budget and enforces bursts of short-term restriction on small communities instead of long-term isolation of unaffected individuals. In comparison to baselines, ComMit reduces the peak of infection by 73% and shortens the duration of infection by 90%, even for persistent spreads

Perturb and Combine to Identify Influential Spreaders in Real-World Networks

International audienceGraph degeneracy algorithms were recently shown to be very effective at detecting the influential spreaders in a network. However, degeneracy-based decompositions of a graph are unstable to small perturbations of the network structure. At the same time, it is well-known in Machine Learning that the performance of unstable algorithms can be greatly improved by using Perturb and Combine (P&C) strategies. Motivated by these observations, we propose a P&C procedure for networks that (1) creates many perturbed versions of a given graph, (2) applies the same algorithm separately to each graph, and (3) combines the results. Experiments conducted on benchmark datasets of real-world networks reveal that our strategy improves the performance of all algorithms tested. Moreover, this performance boost can be obtained at almost no extra cost through parallelization. We finally provide insights as to why our strategy is effective from a theoretical perspective. To the best of our knowledge, this work is the first application of P&C to networks

An Initial Framework Assessing the Safety of Complex Systems

Trabajo presentado en la Conference on Complex Systems, celebrada online del 7 al 11 de diciembre de 2020.Atmospheric blocking events, that is large-scale nearly stationary atmospheric pressure patterns, are often associated with extreme weather in the mid-latitudes, such as heat waves and cold spells which have significant consequences on ecosystems, human health and economy. The high impact of blocking events has motivated numerous studies. However, there is not yet a comprehensive theory explaining their onset, maintenance and decay and their numerical prediction remains a challenge. In recent years, a number of studies have successfully employed complex network descriptions of fluid transport to characterize dynamical patterns in geophysical flows. The aim of the current work is to investigate the potential of so called Lagrangian flow networks for the detection and perhaps forecasting of atmospheric blocking events. The network is constructed by associating nodes to regions of the atmosphere and establishing links based on the flux of material between these nodes during a given time interval. One can then use effective tools and metrics developed in the context of graph theory to explore the atmospheric flow properties. In particular, Ser-Giacomi et al. [1] showed how optimal paths in a Lagrangian flow network highlight distinctive circulation patterns associated with atmospheric blocking events. We extend these results by studying the behavior of selected network measures (such as degree, entropy and harmonic closeness centrality)at the onset of and during blocking situations, demonstrating their ability to trace the spatio-temporal characteristics of these events.This research was conducted as part of the CAFE (Climate Advanced Forecasting of sub-seasonal Extremes) Innovative Training Network which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813844

Networked Dynamical Systems: Privacy, Control, and Cognition

Many natural and man-made systems, ranging from thenervous system to power and transportation grids to societies, exhibitdynamic behaviors that evolve over a sparse and complex network. This networked aspect raises significant challenges and opportunities for the identification, analysis, and control of such dynamic behaviors. While some of these challenges emanate from the networked aspect \emph{per se} (such as the sparsity of connections between system components and the interplay between nodal \emph{communication} and network dynamics), various challenges arise from the specific application areas (such as privacy concerns in cyber-physical systems or the need for \emph{scalable} algorithm designs due to the large size of various biological and engineered networks). On the other hand, networked systems provide significant opportunities and allow for performance and robustness levels that are far beyond reach for centralized systems, with examples ranging from the Internet (of Things) to the smart grid and the brain. This dissertation aims to address several of these challenges and harness these opportunities. The dissertation is divided into three parts. In the first part, we study privacy concerns whose resolution is vital for the utility of networked cyber-physical systems. We study the problems of average consensus and convex optimization as two principal distributed computations occurring over networks and design algorithm with rigorous privacy guarantees that provide a \emph{best achievable} tradeoff between network utility and privacy. In the second part, we analyze networks with resource constraints. More specifically, we study three problems of stabilization under communication (bandwidth and latency) limitations in sensing and actuation, optimal time-varying control scheduling problem under limited number of actuators and control energy, and the structure identification problem of under-sensed networks (i.e., networks with latent nodes). Finally in the last part, we focus on the intersection of networked dynamical systems and neuroscience and draw connections between brain network dynamics and two extensively studied but yet not fully understood neuro-cognitive phenomena: goal-driven selective attention and neural oscillations. Using a novel axiomatic approach, we establish these connections in the form of necessary and/or sufficient conditions on the network structure that match the network output trajectories with experimentally observed brain activity

Disruption and disease: How does population management affect disease risk in wild bird populations?

Despite the ubiquity of wildlife management, from reintroductions and supplemental feeding to culling and habitat destruction, very little is known of the effects of management practices on species’ social dynamics. Species’ social structure has the potential to affect not only behaviour and evolution but also the transmission of information or disease. Understanding the effects of population management on social behaviour and organisation is a key step in understanding these species’ ecology. This thesis examines the differences between individuals’ roles in the social structure and what this means for the transmission of disease. It demonstrates how similarity in movement behaviour scales with increasing social circles, how seasonality in movement and seasonality in association rates covary as well as detailing post-cull behavioural changes. It finds that there is the potential for certain individuals (most likely non-breeding individuals) to transmit infection far and wide. It reveals the similarities in movement behaviour and body condition that birds share with their pair and social group. It emphasises the importance of autumn and winter movement in the transmission of infectious disease and it follows the short- and long-term changes in social structure and movement behaviour following a cull. Cull survivors were observed to retain a higher proportion of associations with their previous associates and moved less far in the year following the cull than in the year preceding it. This is the first application of social network analysis to quantify social structure before and after culling. The findings suggest that culling an infected population may facilitate rather than constrain the transmission of disease

2011 GREAT Day Program

SUNY Geneseo’s Fifth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1005/thumbnail.jp

Structural Results and Applications for Perturbed Markov Chains

Each day, most of us interact with a myriad of networks: we search for information on the web, connect with friends on social media platforms, and power our homes using the electrical grid. Many of these interactions have improved our lives, but some have caused new societal issues - social media facilitating the rise of fake news, for example. The goal of this thesis is to advance our understanding of these systems, in hopes improving beneficial interactions with networks while reducing the harm of detrimental ones. Our primary contributions are threefold. First, we devise new algorithms for estimating Personalized PageRank (PPR), a measure of similarity between the nodes in a network used in applications like web search and recommendation systems. In contrast to most existing PPR estimators, our algorithms exploit local graph structure to reduce estimation complexity. We show the analysis of such algorithms is tractable for certain random graph models, and that the key insights obtained from these models hold empirically for real graphs. Our second contribution is to apply ideas from the PPR literature to two other problems. First, we show that PPR estimators can be adapted to the policy evaluation problem in reinforcement learning. More specifically, we devise policy evaluation algorithms inspired by existing PPR estimators that leverage certain side information to reduce the sample complexity of existing methods. Second, we use analytical ideas from the PPR literature to show that convergence behavior and robustness are intimately related for a certain class of Markov chains. Finally, we study social learning over networks as a model for the spread of fake news. For this model, we characterize the learning outcome in terms of a novel measure of the “density” of users spreading fake news. Using this characterization, we also devise optimal strategies for seeding fake news spreaders so as to disrupt learning. These strategies empirically outperform intuitive heuristics on real social networks (despite not being provably optimal for such graphs) and thus provide new insights regarding vulnerabilities in social learning. While the topics studied in this thesis are diverse, a unifying mathematical theme is that of perturbed Markov chains. This includes perturbations that yield useful interpretations in various applications, that provide algorithmic and analytical advantages, and that disrupt some underlying system or process. Throughout the thesis, the perturbed Markov chain theme guides our analysis and suggests more general methodologies.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155213/1/dvial_1.pd

Modelling Hospital Acquired Clostridium difficile Infections And Its Transmission In Acute Hospital Settings

The thesis explored a number of fundamental issues regarding the development of predictive models for hospital acquired Clostridium difficile infection (HA CDI) and its outbreaks. As predictive modeling for hospital acquired infection is still an emerging field and the ability to analyse HA CDI and potential outbreaks are in a developmental stage, the research documented in this thesis is exploratory and preliminary. Predictive modeling for the outbreak of hospital acquired infections can be considered at two levels: population and individual. We provide a comprehensive review regarding modeling methodology in this field at both population level and individual level. The transmission of HA CDI is not well understood. An agent based simulation model was built to evaluate the relative importance of the potential sources of Clostridium difficile (C. difficile) infection in a non-outbreak ward setting in an acute care hospital. The model was calibrated through a two stage procedure which utilized Latin Hypercube Sampling methodology and Genetic Algorithm optimization to capture five different patterns reported in the literature. A number of aspects of the model including housekeeping, hand hygiene compliance, patient turnover, and antibiotic pressure were explored. Based on the modeling results, several prevention policies are recommended. One widely used tool to better understand the dynamics of infectious disease outbreaks is network epidemiology. We explored the potential of using network statistics for the prediction of the transmission of HA CDIs in the hospital. Two types of dynamic networks were studied: ward level contacts and hospital transfers. An innovative method that combines time series data mining and predictive classification models was introduced for the analysis of these dynamic networks and for the prediction of HA CDI transmission. The results suggest that the network statistics extracted from the dynamic networks are potential predictors for the transmission of HA CDIs. We explored the potential of using the “multiple modeling methods approach” to predict HA CDI patient at risk by using the data from the information systems in the hospital. A range of machine learning predictive models were utilized to analyse collected data from a hospital. Our results suggest that the multiple modeling methods approach is able to improve prediction performance and to reveal new insights in the data set. We recommend that this approach might be considered for future studies on the predictive model construction and risk factor analysis

Topology Reconstruction of Dynamical Networks via Constrained Lyapunov Equations

The network structure (or topology) of a dynamical network is often unavailable or uncertain. Hence, we consider the problem of network reconstruction. Network reconstruction aims at inferring the topology of a dynamical network using measurements obtained from the network. In this technical note we define the notion of solvability of the network reconstruction problem. Subsequently, we provide necessary and sufficient conditions under which the network reconstruction problem is solvable. Finally, using constrained Lyapunov equations, we establish novel network reconstruction algorithms, applicable to general dynamical networks. We also provide specialized algorithms for specific network dynamics, such as the well-known consensus and adjacency dynamics.Comment: 8 page