89,644 research outputs found
Revisiting Content Availability in Distributed Online Social Networks
Online Social Networks (OSN) are among the most popular applications in
today's Internet. Decentralized online social networks (DOSNs), a special class
of OSNs, promise better privacy and autonomy than traditional centralized OSNs.
However, ensuring availability of content when the content owner is not online
remains a major challenge. In this paper, we rely on the structure of the
social graphs underlying DOSN for replication. In particular, we propose that
friends, who are anyhow interested in the content, are used to replicate the
users content. We study the availability of such natural replication schemes
via both theoretical analysis as well as simulations based on data from OSN
users. We find that the availability of the content increases drastically when
compared to the online time of the user, e. g., by a factor of more than 2 for
90% of the users. Thus, with these simple schemes we provide a baseline for any
more complicated content replication scheme.Comment: 11pages, 12 figures; Technical report at TU Berlin, Department of
Electrical Engineering and Computer Science (ISSN 1436-9915
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
Method maximizing the spread of influence in directed signed weighted graphs
We propose a new method for maximizing the spread of influence, based on the identification of significant factors of the total energy of a control system. The model of a socio-economic system can be represented in the form of cognitive maps that are directed signed weighted graphs with cause-and-effect relationships and cycles. Identification and selection of target factors and effective control factors of a system is carried out as a solution to the optimal control problem. The influences are determined by the solution to optimization problem of maximizing the objective function, leading to matrix symmetrization. The gear-ratio symmetrization is based on computing the similarity extent of fan-beam structures of the influence spread of vertices v_i and v_j to all other vertices. This approach provides the real computational domain and correctness of solving the optimal control problem. In addition, it does not impose requirements for graphs to be ordering relationships, to have a matrix of special type or to fulfill stability conditions. In this paper, determination of new metrics of vertices, indicating and estimating the extent and the ability to effectively control, are likewise offered. Additionally, we provide experimental results over real cognitive models in support
A model for cascading failures in complex networks
Large but rare cascades triggered by small initial shocks are present in most
of the infrastructure networks. Here we present a simple model for cascading
failures based on the dynamical redistribution of the flow on the network. We
show that the breakdown of a single node is sufficient to collapse the
efficiency of the entire system if the node is among the ones with largest
load. This is particularly important for real-world networks with an highly
hetereogeneous distribution of loads as the Internet and electrical power
grids.Comment: 4 pages, 4 figure
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