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      Phase transition in a sequential assignment problem on graphs

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          Abstract

          We study the following game on a finite graph \(G = (V, E)\). At the start, each edge is assigned an integer \(n_e \ge 0\), \(n = \sum_{e \in E} n_e\). In round \(t\), \(1 \le t \le n\), a uniformly random vertex \(v \in V\) is chosen and one of the edges \(f\) incident with \(v\) is selected by the player. The value assigned to \(f\) is then decreased by \(1\). The player wins, if the configuration \((0, \dots, 0)\) is reached; in other words, the edge values never go negative. Our main result is that there is a phase transition: as \(n \to \infty\), the probability that the player wins approaches a constant \(c_G > 0\) when \((n_e/n : e \in E)\) converges to a point in the interior of a certain convex set \(\mathcal{R}_G\), and goes to \(0\) exponentially when \((n_e/n : e \in E)\) is bounded away from \(\mathcal{R}_G\). We also obtain upper bounds in the near-critical region, that is when \((n_e/n : e \in E)\) lies close to \(\partial \mathcal{R}_G\). We supply quantitative error bounds in our arguments.

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          Controlling edge dynamics in complex networks

          The interaction of distinct units in physical, social, biological and technological systems naturally gives rise to complex network structures. Networks have constantly been in the focus of research for the last decade, with considerable advances in the description of their structural and dynamical properties. However, much less effort has been devoted to studying the controllability of the dynamics taking place on them. Here we introduce and evaluate a dynamical process defined on the edges of a network, and demonstrate that the controllability properties of this process significantly differ from simple nodal dynamics. Evaluation of real-world networks indicates that most of them are more controllable than their randomized counterparts. We also find that transcriptional regulatory networks are particularly easy to control. Analytic calculations show that networks with scale-free degree distributions have better controllability properties than uncorrelated networks, and positively correlated in- and out-degrees enhance the controllability of the proposed dynamics.
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            Emergence of bimodality in controlling complex networks

            Our ability to control complex systems is a fundamental challenge of contemporary science. Recently introduced tools to identify the driver nodes, nodes through which we can achieve full control, predict the existence of multiple control configurations, prompting us to classify each node in a network based on their role in control. Accordingly a node is critical, intermittent or redundant if it acts as a driver node in all, some or none of the control configurations. Here we develop an analytical framework to identify the category of each node, leading to the discovery of two distinct control modes in complex systems: centralized vs distributed control. We predict the control mode for an arbitrary network and show that one can alter it through small structural perturbations. The uncovered bimodality has implications from network security to organizational research and offers new insights into the dynamics and control of complex systems.
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              A Sequential Stochastic Assignment Problem

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                Author and article information

                Journal
                1507.04169

                Numerical methods,Probability
                Numerical methods, Probability

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