Percolation and Connection Times in Multi-scale Dynamic Networks


Book Description

We study the effects of mobility on two crucial characteristics in multi-scale dynamic networks: percolation and connection times. Our analysis provides insights into the question, to what extent long-time averages are well-approximated by the expected values of the corresponding quantities, i.e., the percolation and connection probabilities. In particular, we show that in multi-scale models, strong random effects may persist in the limit. Depending on the precise model choice, these may take the form of a spatial birth-death process or a Brownian motion. Despite the variety of structures that appear in the limit, we show that they can be tackled in a common framework with the potential to be applicable more generally in order to identify limits in dynamic spatial network models going beyond the examples considered in the present work.




Connection Intervals in Multi-scale Dynamic Networks


Book Description

We consider a hybrid spatial communication system in which mobile nodes can connect to static sinks in a bounded number of intermediate relaying hops. We describe the distribution of the connection intervals of a typical mobile node, i.e., the intervals of uninterrupted connection to the family of sinks. This is achieved in the limit of many hops, sparse sinks and growing time horizons.We identify three regimes illustrating that the limiting distribution depends sensitively on the scaling of the time horizon.




Social Interaction in Animals: Linking Experimental Approach and Social Network Analysis


Book Description

Understanding the link between individual behaviour and population organization and functioning has long been central to ecology and evolutionary biology. Behaviour is a response to intrinsic and extrinsic factors including individual state, ecological factors or social interactions. Within a group, each individual can be seen as part of a network of social interactions varying in strength, type and dynamic. The structure of this network can deeply impact the ecology and evolution of individuals, populations and species. Within a group social interactions can take many forms and may significantly affect an individual’s fitness. These interactions may result in complex systems at the group-level, such as in the case of collective decisions (to migrate, to build nest or to forage). Among them, social transmission of information has been studied mostly in vertebrates: fish, birds and mammals including humans. In insects, social learning has been unambiguously demonstrated in social Hymenoptera but this probably reflects limited research effort and recent evidence show that even non-eusocial insects such as Drosophila, cockroaches and crickets can copy the behaviour of others. Compared to individual learning, which requires a trial and error period every generation, social learning can potentially result in the stable transmission of behaviours across generations, leading to cultural traditions in some species. The study of the processes which may facilitate or prevent this transmission and the analyses of the relationship between social network structure and efficiency of social transmission became these recent years an emerging and promising field of research. The goal of this research topic is to present the genetic and socio-environmental factors affecting social interaction and information or pathogen transmission with the integration of experimental approaches, social network analyses and modelling. Importantly, we aim to understand whether a relationship between social network structures and dynamics can reflect the efficiency of social transmission, i.e. can we use social network analysis to predict the social transmission of information or of pathogen, collective decision-making and ultimately the evolutionary trajectory of a group?










Proceedings of the Workshop, Intrinsic Multiscale Structure and Dynamics in Complex Electronic Oxides


Book Description

There is considerable interest in the intrinsically multiscale structure and dynamics of complex electronic oxides, especially since these materials include those of technological importance, such as colossal magnetoresistance manganites and cuprate high temperature superconductors. Current microscopies, such as diffuse X-ray and inelastic neutron scattering, electromagnetic and acoustic response, NMR and scanning tunneling microscope probes, have revealed static and dynamic multiscale patterns in charge positioning, lattice structure and magnetic orientation, that respond to both external stress and magnetic field. These self-organized patterns include charge and orbital ordering; stripes in strain/spin; and labyrinth-like conductance modulations. The materials exhibit nanoscale phase segregation and mesoscale inhomogeneous clustering, and their phase transitions can have a percolative character.This volume gathers experimental and theoretical work on these exciting new developments in condensed matter physics and materials science.




Criticality as a signature of healthy neural systems: multi-scale experimental and computational studies


Book Description

Since 2003, when spontaneous activity in cortical slices was first found to follow scale-free statistical distributions in size and duration, increasing experimental evidences and theoretical models have been reported in the literature supporting the emergence of evidence of scale invariance in the cortex. Although strongly debated, such results refer to many different in vitro and in vivo preparations (awake monkeys, anesthetized rats and cats, in vitro slices and dissociated cultures), suggesting that power law distributions and scale free correlations are a very general and robust feature of cortical activity that has been conserved across species as specific substrate for information storage, transmission and processing. Equally important is that the features reminiscent of scale invariance and criticality are observed at scale spanning from the level of interacting arrays of neurons all the way up to correlations across the entire brain. Thus, if we accept that the brain operates near a critical point, little is known about the causes and/or consequences of a loss of criticality and its relation with brain diseases (e.g. epilepsy). The study of how pathogenetical mechanisms are related to the critical/non-critical behavior of neuronal networks would likely provide new insights into the cellular and synaptic determinants of the emergence of critical-like dynamics and structures in neural systems. At the same time, the relation between the impaired behavior and the disruption of criticality would help clarify its role in normal brain function. The main objective of this Research Topic is to investigate the emergence/disruption of the emergent critical-like states in healthy/impaired neural systems.




Advances in Intelligent Data Analysis XVIII


Book Description

This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.




Dynamical Systems on Networks


Book Description

This volume is a tutorial for the study of dynamical systems on networks. It discusses both methodology and models, including spreading models for social and biological contagions. The authors focus especially on “simple” situations that are analytically tractable, because they are insightful and provide useful springboards for the study of more complicated scenarios. This tutorial, which also includes key pointers to the literature, should be helpful for junior and senior undergraduate students, graduate students, and researchers from mathematics, physics, and engineering who seek to study dynamical systems on networks but who may not have prior experience with graph theory or networks. Mason A. Porter is Professor of Nonlinear and Complex Systems at the Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, UK. He is also a member of the CABDyN Complexity Centre and a Tutorial Fellow of Somerville College. James P. Gleeson is Professor of Industrial and Applied Mathematics, and co-Director of MACSI, at the University of Limerick, Ireland.