450,721 research outputs found
Asymptotics of classical spin networks
A spin network is a cubic ribbon graph labeled by representations of
. Spin networks are important in various areas of Mathematics
(3-dimensional Quantum Topology), Physics (Angular Momentum, Classical and
Quantum Gravity) and Chemistry (Atomic Spectroscopy). The evaluation of a spin
network is an integer number. The main results of our paper are: (a) an
existence theorem for the asymptotics of evaluations of arbitrary spin networks
(using the theory of -functions), (b) a rationality property of the
generating series of all evaluations with a fixed underlying graph (using the
combinatorics of the chromatic evaluation of a spin network), (c) rigorous
effective computations of our results for some -symbols using the
Wilf-Zeilberger theory, and (d) a complete analysis of the regular Cube
spin network (including a non-rigorous guess of its Stokes constants), in the
appendix.Comment: 24 pages, 32 figure
The geometry of spontaneous spiking in neuronal networks
The mathematical theory of pattern formation in electrically coupled networks
of excitable neurons forced by small noise is presented in this work. Using the
Freidlin-Wentzell large deviation theory for randomly perturbed dynamical
systems and the elements of the algebraic graph theory, we identify and analyze
the main regimes in the network dynamics in terms of the key control
parameters: excitability, coupling strength, and network topology. The analysis
reveals the geometry of spontaneous dynamics in electrically coupled network.
Specifically, we show that the location of the minima of a certain continuous
function on the surface of the unit n-cube encodes the most likely activity
patterns generated by the network. By studying how the minima of this function
evolve under the variation of the coupling strength, we describe the principal
transformations in the network dynamics. The minimization problem is also used
for the quantitative description of the main dynamical regimes and transitions
between them. In particular, for the weak and strong coupling regimes, we
present asymptotic formulas for the network activity rate as a function of the
coupling strength and the degree of the network. The variational analysis is
complemented by the stability analysis of the synchronous state in the strong
coupling regime. The stability estimates reveal the contribution of the network
connectivity and the properties of the cycle subspace associated with the graph
of the network to its synchronization properties. This work is motivated by the
experimental and modeling studies of the ensemble of neurons in the Locus
Coeruleus, a nucleus in the brainstem involved in the regulation of cognitive
performance and behavior
An Alternative Approach to the Calculation and Analysis of Connectivity in the World City Network
Empirical research on world cities often draws on Taylor's (2001) notion of
an 'interlocking network model', in which office networks of globalized service
firms are assumed to shape the spatialities of urban networks. In spite of its
many merits, this approach is limited because the resultant adjacency matrices
are not really fit for network-analytic calculations. We therefore propose a
fresh analytical approach using a primary linkage algorithm that produces a
one-mode directed graph based on Taylor's two-mode city/firm network data. The
procedure has the advantage of creating less dense networks when compared to
the interlocking network model, while nonetheless retaining the network
structure apparent in the initial dataset. We randomize the empirical network
with a bootstrapping simulation approach, and compare the simulated parameters
of this null-model with our empirical network parameter (i.e. betweenness
centrality). We find that our approach produces results that are comparable to
those of the standard interlocking network model. However, because our approach
is based on an actual graph representation and network analysis, we are able to
assess cities' position in the network at large. For instance, we find that
cities such as Tokyo, Sydney, Melbourne, Almaty and Karachi hold more strategic
and valuable positions than suggested in the interlocking networks as they play
a bridging role in connecting cities across regions. In general, we argue that
our graph representation allows for further and deeper analysis of the original
data, further extending world city network research into a theory-based
empirical research approach.Comment: 18 pages, 9 figures, 2 table
Modelling Interdependent Cascading Failures in Real World Complex Networks using a Functional Dependency Model
Infrastructure systems are becoming increasingly complex and interdependent. As a result our ability to predict the
likelihood of large-scale failure of these systems has significantly diminished and the consequence of this is that we
now have a greatly increased risk of devastating impacts to society.
Traditionally these systems have been analysed using physically-based models. However, this approach can only
provide information for a specific network and is limited by the number of scenarios that can be tested. In an attempt
to overcome this shortcoming, many studies have used network graph theory to provide an alternative analysis
approach. This approach has tended to consider infrastructure systems in isolation, but has recently considered
the analysis of interdependent networks through combination with percolation theory. However, these studies have
focused on the analysis of synthetic networks and tend to only consider the topology of the system.
In this paper we develop a new analysis approach, based upon network theory, but accounting for the hierarchical
structure and functional dependency observed in real world infrastructure networks. We apply this method to two
real world networks, to show that it can be used to quantify the impact that failures within an electricity network have
upon a dependent water network
Evaluation of Whole-Brain Resting-State Functional Connectivity in Spinal Cord Injury - A Large-Scale Network Analysis Using Network Based Statistic
Large-scale network analysis characterizes the brain as a complex network of nodes and edges to evaluate functional connectivity patterns. The utility of graph-based techniques has been demonstrated in an increasing number of restingstate functional MRI (rs-fMRI) studies in the normal and diseased brain. However, to our knowledge, graph theory has not been used to study the reorganization pattern of resting-state brain networks in patients with traumatic complete spinal cord injury (SCI). In the present analysis, we applied a graph-theoretical approach to explore changes to global brain network architecture as a result of SCI. Fifteen subjects with chronic (\u3e 2 years) complete (American Spinal Injury Association [ASIA] A) cervical SCI and 15 neurologically intact controls were scanned using rs-fMRI. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI) or nodes. The average time series was extracted at each node, and correlation analysis was performed between every pair of nodes. A functional connectivity matrix for each subject was then generated. Subsequently, the matrices were averaged across groups, and network changes were evaluated between groups using the network-based statistic (NBS) method. Our results showed decreased connectivity in a subnetwork of the whole brain in SCI compared with control subjects. Upon further examination, increased connectivity was observed in a subnetwork of the sensorimotor cortex and cerebellum network in SCI. In conclusion, our findings emphasize the applicability of NBS to study functional connectivity architecture in diseased brain states. Further, we show reorganization of large-scale resting-state brain networks in traumatic SCI, with potential prognostic and therapeutic implications
Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches
Finding the common structural brain connectivity network for a given
population is an open problem, crucial for current neuro-science. Recent
evidence suggests there's a tightly connected network shared between humans.
Obtaining this network will, among many advantages , allow us to focus
cognitive and clinical analyses on common connections, thus increasing their
statistical power. In turn, knowledge about the common network will facilitate
novel analyses to understand the structure-function relationship in the brain.
In this work, we present a new algorithm for computing the core structural
connectivity network of a subject sample combining graph theory and statistics.
Our algorithm works in accordance with novel evidence on brain topology. We
analyze the problem theoretically and prove its complexity. Using 309 subjects,
we show its advantages when used as a feature selection for connectivity
analysis on populations, outperforming the current approaches
Physics-inspired Performace Evaluation of a Structured Peer-to-Peer Overlay Network
In the majority of structured peer-to-peer overlay networks a graph
with a desirable topology is constructed. In most cases, the graph is
maintained by a periodic activity performed by each node in the graph
to preserve the desirable structure in face of the continuous change
of the set of nodes. The interaction of the autonomous periodic
activities of the nodes renders the performance analysis of such
systems complex and simulation of scales of interest can be
prohibitive. Physicists, however, are accustomed to dealing with
scale by characterizing a system using intensive variables,
i.e. variables that are size independent. The approach has proved its
usefulness when applied to satisfiability theory. This
work is the first attempt to apply it in the area of distributed
systems. The contribution of this paper is two-fold. First, we
describe a methodology to be used for analyzing the performance of
large scale distributed systems. Second, we show how we applied the
methodology to find an intensive variable that describe the
characteristic behavior of the Chord overlay network, namely, the
ratio of the magnitude of perturbation of the network (joins/failures)
to the magnitude of periodic stabilization of the network
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