The process of pattern formation for a multispecies model anchored on a time varying network is studied. A nonhomogeneous perturbation superposed to an homogeneous stable fixed point can be amplified following the Turing mechanism of instability, solely instigated by the network dynamics. By properly tuning the frequency of the imposed network evolution, one can make the examined system behave as its averaged counterpart, over a finite time window. This is the key observation to derive a closed analytical prediction for the onset of the instability in the time dependent framework. Continuously and piecewise constant periodic time varying networks are analyzed, setting the framework for the proposed approach. The extension to nonperiodic settings is also discussed.

In this paper a novel analytical model for the saturation throughput of unslotted Carrier Sensing Multiple Access with Collision Avoidance (CSMA/CA) in wireless networks is proposed. A fixed point procedure is developed based on the interaction of the Physical layer (PHY) and the Medium Access Control sub-layer (MAC). The output of the Clear Channel Assessment (CCA), i.e. idle or busy medium in the neighborhood of a node, serves as a feedback mechanism for the dynamical scheduling rate controlled by the back-off procedure. The PHY is described by a renewal process between successful transmissions with failed attempts and collided packets in between. A semi-Markov process of the internal states of a node is used as a model for the MAC. An event-driven simulator for the non-beacon enabled IEEE Std 802.15.4™MAC is developed to verify the numerical results of the analytical method. A detailed analysis of the idle period after a transmission is carried out based on the proposed analytical approach. The probability that the CCA senses the channel idle depends clearly on the actual back-off stage and the first back-off expiration after a transmission cannot be modeled by a exponential distribution when a finite number of nodes are in contention. The output of the event-driven simulations confirms both statements in great detail and the saturated throughput evaluated B. Lauwens ( ) · B. Scheers CISS, Royal Military Academy, 30 Renaissancelaan,

We consider random walks on dynamical networks where edges appear and disappear during finite time intervals. The process is grounded on three independent stochastic processes determining the walker's waiting-time, the up-time and down-time of edges activation. We first propose a comprehensive analytical and numerical treatment on directed acyclic graphs. Once cycles are allowed in the network, non-Markovian trajectories may emerge, remarkably even if the walker and the evolution of the network edges are governed by memoryless Poisson processes. We then introduce a general analytical framework to characterize such non-Markovian walks and validate our findings with numerical simulations.

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Identifying and detecting disinformation is a major challenge. Twitter provides datasets of disinformation campaigns through their information operations report. We compare the results of community detection using a classical network representation with a maximum entropy network model. We conclude that the latter method is useful to identify the most significant interactions in the disinformation network over multiple datasets. We also apply the method to a disinformation dataset related to COVID-19, which allows us to assess the repeatability of studies on disinformation datasets.