Network resilience

Many systems on our planet are known to shift abruptly and irreversibly from one state to another when they are forced across a "tipping point," such as mass extinctions in ecological networks, cascading failures in infrastructure systems, and social convention changes in human and animal networks. Such a regime shift demonstrates a system's resilience that characterizes the ability of a system to adjust its activity to retain its basic functionality in the face of internal disturbances or external environmental changes. In the past 50 years, attention was almost exclusively given to low dimensional systems and calibration of their resilience functions and indicators of early warning signals without considerations for the interactions between the components. Only in recent years, taking advantages of the network theory and lavish real data sets, network scientists have directed their interest to the real-world complex networked multidimensional systems and their resilience function and early warning indicators. This report is devoted to a comprehensive review of resilience function and regime shift of complex systems in different domains, such as ecology, biology, social systems and infrastructure. We cover the related research about empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also discuss some ambiguous definitions, such as robustness, resilience, and stability.Comment: Review chapter

Complexity Heliophysics: A lived and living history of systems and complexity science in Heliophysics

In this piece we study complexity science in the context of Heliophysics, describing it not as a discipline, but as a paradigm. In the context of Heliophysics, complexity science is the study of a star, interplanetary environment, magnetosphere, upper and terrestrial atmospheres, and planetary surface as interacting subsystems. Complexity science studies entities in a system (e.g., electrons in an atom, planets in a solar system, individuals in a society) and their interactions, and is the nature of what emerges from these interactions. It is a paradigm that employs systems approaches and is inherently multi- and cross-scale. Heliophysics processes span at least 15 orders of magnitude in space and another 15 in time, and its reaches go well beyond our own solar system and Earth's space environment to touch planetary, exoplanetary, and astrophysical domains. It is an uncommon domain within which to explore complexity science. After first outlining the dimensions of complexity science, the review proceeds in three epochal parts: 1) A pivotal year in the Complexity Heliophysics paradigm: 1996; 2) The transitional years that established foundations of the paradigm (1996-2010); and 3) The emergent literature largely beyond 2010. This review article excavates the lived and living history of complexity science in Heliophysics. The intention is to provide inspiration, help researchers think more coherently about ideas of complexity science in Heliophysics, and guide future research. It will be instructive to Heliophysics researchers, but also to any reader interested in or hoping to advance the frontier of systems and complexity science

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

Interim research assessment 2003-2005 - Computer Science

This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

Vers une amélioration de la diffusion des informations dans les réseaux sans-fils

Dans les systèmes d'alertes publiques, l étude de la diffusion des informations dans le réseau est essentielle. Les systèmes de diffusion des messages d'alertes doivent atteindre beaucoup de nœuds en peu de temps. Dans les réseaux de communication basés sur les interactions device to device , on s'est récemment beaucoup intéressé à la diffusion des informations et le besoin d'auto-organisation a été mis en évidence. L'auto-organisation conduit à des comportements locaux et des interactions qui ont un effet sur le réseau global et présentent un avantage de scalabilité. Ces réseaux auto-organisés peuvent être autonomes et utiliser peu d'espace mémoire. On peut développer des caractères auto-organisés dans les réseaux de communication en utilisant des idées venant de phénomènes naturels. Il semble intéressant de chercher à obtenir les propriétés des small world pour améliorer la diffusion des informations dans le réseau. Dans les modèles de small world on réalise un recâblage des liens dans le réseau en changeant la taille et la direction des liens existants. Dans un environnement sans-fils autonome une organisation de ce type peut être créée en utilisant le flocking, l'inhibition latérale et le beamforming . Dans ce but, l'auteur utilise d'abord l'analogie avec l'inhibition latérale, le flocking et le beamforming pour montrer comment la diffusion des informations peut être améliorée. L'analogue de l'inhibition latérale est utilisé pour créer des régions virtuelles dans le réseau. Puis en utilisant l'analogie avec les règles du flocking, on caractérise les propriétés des faisceaux permettant aux nœuds de communiquer dans les régions. Nous prouvons que les propriétés des small world sont vérifiées en utilisant la mesure des moyennes des longueurs des chemins. Cependant l'algorithme proposé est valable pour les réseaux statiques alors que dans les cas introduisant de la mobilité, les concepts d'inhibition latérale et de flocking nécessiteraient beaucoup plus de temps. Dans le cas d'un réseau mobile la structure du réseau change fréquemment. Certaines connexions intermittentes impactent fortement la diffusion des informations. L'auteur utilise le concept de stabilité avec le beamforming pour montrer comment on peut améliorer la diffusion des informations. Dans son algorithme il prévoit d'abord la stabilité du nœud en utilisant des informations locales et il utilise ce résultat pour identifier les nœuds qui réaliseront du beamforming. Dans l'algorithme, les nœuds de stabilité faible sont autorisés à faire du beamforming vers les nœuds de forte stabilité. La frontière entre forte et faible stabilité est fixée par un seuil. Cet algorithme ne nécessite pas une connaissance globale du réseau, mais utilise des données locales. Les résultats sont validés en étudiant le temps au bout duquel plus de nœuds reçoivent l'information et en comparant avec d'autres algorithmes de la littérature. Cependant, dans les réseaux réels, les changements de structure ne sont pas dus qu'à la mobilité, mais également à des changements de la densité des nœuds à un moment donné. Pour tenir compte de l'influence de tels événements sur la diffusion des informations concernant la sécurité publique, l'auteur utilise les concepts de modèle de métapopulation, épidémiologiques, beamforming et mobilité géographique obtenu à partir de données D4D. L'auteur propose la création de trois états latents qu'il ajoute au modèle épidémiologique connu: SIR. L'auteur étudie les états transitoires en analysant l'évolution du nombre de postes ayant reçu les informations et compare les résultats concernant ce nombre dans les différents cas. L'auteur démontre ainsi que le scenario qu'il propose permet d'améliorer le processus de diffusion des informations. Il montre aussi les effets de différents paramètres comme le nombre de sources, le nombre de paquets, les paramètres de mobilité et ceux qui caractérisent les antennes sur la diffusion des informationsIn public warning message systems, information dissemination across the network is a critical aspect that has to be addressed. Dissemination of warning messages should be such that it reaches as many nodes in the network in a short time. In communication networks those based on device to device interactions, dissemination of the information has lately picked up lot of interest and the need for self organization of the network has been brought up. Self organization leads to local behaviors and interactions that have global effects and helps in addressing scaling issues. The use of self organized features allows autonomous behavior with low memory usage. Some examples of self organization phenomenon that are observed in nature are Lateral Inhibition and Flocking. In order to provide self organized features to communication networks, insights from such naturally occurring phenomenon is used. Achieving small world properties is an attractive way to enhance information dissemination across the network. In small world model rewiring of links in the network is performed by altering the length and the direction of the existing links. In an autonomous wireless environment such organization can be achieved using self organized phenomenon like Lateral inhibition and Flocking and beamforming (a concept in communication). Towards this, we first use Lateral Inhibition, analogy to Flocking behavior and beamforming to show how dissemination of information can be enhanced. Lateral Inhibition is used to create virtual regions in the network. Then using the analogy of Flocking rules, beam properties of the nodes in the regions are set. We then prove that small world properties are achieved using average path length metric. However, the proposed algorithm is applicable to static networks and Flocking and Lateral Inhibition concepts, if used in a mobile scenario, will be highly complex in terms of computation and memory. In a mobile scenario such as human mobility aided networks, the network structure changes frequently. In such conditions dissemination of information is highly impacted as new connections are made and old ones are broken. We thus use stability concept in mobile networks with beamforming to show how information dissemination process can be enhanced. In the algorithm, we first predict the stability of a node in the mobile network using locally available information and then uses it to identify beamforming nodes. In the algorithm, the low stability nodes are allowed to beamform towards the nodes with high stability. The difference between high and low stability nodes is based on threshold value. The algorithm is developed such that it does not require any global knowledge about the network and works using only local information. The results are validated using how quickly more number of nodes receive the information and different state of the art algorithms. We also show the effect of various parameters such as number of sources, number of packets, mobility parameters and antenna parameters etc. on the information dissemination process in the network. In realistic scenarios however, the dynamicity in the network is not only related to mobility. Dynamic conditions also arise due to change in density of nodes at a given time. To address effect of such scenario on the dissemination of information related to public safety in a metapopulation, we use the concepts of epidemic model, beamforming and the countrywide mobility pattern extracted from the $D4D$ dataset. Here, we also propose the addition of three latent states to the existing epidemic model ($SIR$ model). We study the transient states towards the evolution of the number of devices having the information and the difference in the number of devices having the information when compared with different cases to evaluate the results. Through the results we show that enhancements in the dissemination process can be achieved in the addressed scenarioEVRY-INT (912282302) / SudocSudocFranceF