Percolation on fitness-dependent networks with heterogeneous resilience.

The ability to understand the impact of adversarial processes on networks is crucial to various disciplines. The objects of study in this article are fitness-driven networks. Fitness-dependent networks are fully described by a probability distribution of fitness and an attachment kernel. Every node in the network is endowed with a fitness value and the attachment kernel translates the fitness of two nodes into the probability that these two nodes share an edge. This concept is also known as mutual attractiveness. In the present article, fitness does not only serve as a measure of attractiveness, but also as a measure of a node's robustness against failure. The probability that a node fails increases with the number of failures in its direct neighborhood and decreases with higher fitness. Both static and dynamic network models are considered. Analytical results for the percolation threshold and the occupied fraction are derived. One of the results is that the distinction between the dynamic and the static model has a profound impact on the way failures spread over the network. Additionally, we find that the introduction of mutual attractiveness stabilizes the network compared to a pure random attachment.

Mutual selection in time-varying networks.

Time-varying networks play an important role in the investigation of the stochastic processes that occur on complex networks. The ability to formulate the development of the network topology on the same time scale as the evolution of the random process is important for a variety of applications, including the spreading of diseases. Past contributions have investigated random processes on time-varying networks with a purely random attachment mechanism. The possibility of extending these findings towards a time-varying network that is driven by mutual attractiveness is explored in this paper. Mutual attractiveness models are characterized by a linking function that describes the probability of the existence of an edge, which depends mutually on the attractiveness of the nodes on both ends of that edge. This class of attachment mechanisms has been considered before in the fitness-based complex networks literature but not on time-varying networks. Also, the impact of mutual selection is investigated alongside opinion formation and epidemic outbreaks. We find closed-form solutions for the quantities of interest using a factorizable linking function. The voter model exhibits an unanticipated behavior as the network never reaches consensus in the case of mutual selection but stays forever in its initial macroscopic configuration, which is a further piece of evidence that time-varying networks differ markedly from their static counterpart with respect to random processes that take place on them. We also find that epidemic outbreaks are accelerated by uncorrelated mutual selection compared to previously considered random attachment.

Network growth model with intrinsic vertex fitness.

We study a class of network growth models with attachment rules governed by intrinsic node fitness. Both the individual node degree distribution and the degree correlation properties of the network are obtained as functions of the network growth rules. We also find analytical solutions to the inverse, design, problems of matching the growth rules to the required (e.g., power-law) node degree distribution and more generally to the required degree correlation function. We find that the design problems do not always have solutions. Among the specific conditions on the existence of solutions to the design problems is the requirement that the node degree distribution has to be broader than a certain threshold and the fact that factorizability of the correlation functions requires singular distributions of the node fitnesses. More generally, the restrictions on the input distributions and correlations that ensure solvability of the design problems are expressed in terms of the analytical properties of their generating functions.

Growing trees in internet news groups and forums.

We present an empirical study of the networks created by users within internet news groups and forums and show that they organize themselves into scale-free trees. The structure of these trees depends on the topic under discussion; specialist topics have trees with a short shallow structure whereas more universal topics are discussed widely and have a deeper tree structure. For news groups we find that the distribution of the time intervals between when a message is posted and when it receives a response exhibits a composite power-law behavior. From our statistics we can see if the news group or forum is free or is overseen by a moderator. The correlation function of activity, the number of messages posted in a given time, shows long-range correlations connected with the users' daily routines. The distribution of distances between each message and its root is exponential for most news groups and power law for the forums. For both formats we find that the relation between the supremacy (the total number of nodes that are under the node i, including node i) and the degree is linear s(k) approximately k, in contrast to the analytical relation for the Barabási-Albert network.

Complex growing networks with intrinsic vertex fitness.

One of the major questions in complex network research is to identify the range of mechanisms by which a complex network can self organize into a scale-free state. In this paper we investigate the interplay between a fitness linking mechanism and both random and preferential attachment. In our models, each vertex is assigned a fitness x, drawn from a probability distribution rho(x). In Model A, at each time step a vertex is added and joined to an existing vertex, selected at random, with probability p and an edge is introduced between vertices with fitnesses x and y, with a rate f(x,y), with probability 1-p. Model B differs from Model A in that, with probability p, edges are added with preferential attachment rather than randomly. The analysis of Model A shows that, for every fixed fitness x, the network's degree distribution decays exponentially. In Model B we recover instead a power-law degree distribution whose exponent depends only on p, and we show how this result can be generalized. The properties of a number of particular networks are examined.

Network properties of written human language.

We investigate the nature of written human language within the framework of complex network theory. In particular, we analyze the topology of Orwell's "1984" focusing on the local properties of the network, such as the properties of the nearest neighbors and the clustering coefficient. We find a composite power law behavior for both the average nearest neighbor's degree and average clustering coefficient as a function of the vertex degree. This implies the existence of different functional classes of vertices. Furthermore, we find that the second order vertex correlations are an essential component of the network architecture. To model our empirical results we extend a previously introduced model for language due to Dorogovtsev and Mendes. We propose an accelerated growing network model that contains three growth mechanisms: linear preferential attachment, local preferential attachment, and the random growth of a predetermined small finite subset of initial vertices. We find that with these elementary stochastic rules we are able to produce a network showing syntacticlike structures.

Spin-glass phase transition on scale-free networks.

We study the Ising spin-glass model on scale-free networks generated by the static model using the replica method. Based on the replica-symmetric solution, we derive the phase diagram consisting of the paramagnetic (P), ferromagnetic (F), and spin glass (SG) phases as well as the Almeida-Thouless line as functions of the degree exponent lambda, the mean degree K, and the fraction of ferromagnetic interactions r. To reflect the inhomogeneity of vertices, we modify the magnetization m and the spin-glass order parameter q with vertex- weights. The transition temperature T(c) (T(g)) between the P-F (P-SG) phases and the critical behaviors of the order parameters are found analytically. When 21/2, while it is in the SG phase at r=1/2. m and q decay as power-laws with increasing temperature with different lambda-dependent exponents. When lambda>3, the T(c) and T(g) are finite and related to the percolation threshold. The critical exponents associated with m and q depend on lambda for 3

Traffic on complex networks: Towards understanding global statistical properties from microscopic density fluctuations.

We study the microscopic time fluctuations of traffic load and the global statistical properties of a dense traffic of particles on scale-free cyclic graphs. For a wide range of driving rates R the traffic is stationary and the load time series exhibits antipersistence due to the regulatory role of the superstructure associated with two hub nodes in the network. We discuss how the superstructure affects the functioning of the network at high traffic density and at the jamming threshold. The degree of correlations systematically decreases with increasing traffic density and eventually disappears when approaching a jamming density R(c). Already before jamming we observe qualitative changes in the global network-load distributions and the particle queuing times. These changes are related to the occurrence of temporary crises in which the network-load increases dramatically, and then slowly falls back to a value characterizing free flow.

Bose-Einstein condensation in random directed networks.

We consider the phenomenon of Bose-Einstein condensation in a random growing directed network. The network grows by the addition of vertices and edges. At each time step the network gains a vertex with probability p and an edge with probability 1-p. The new vertex has a fitness (a,b) a,b>0, with probability f(a,b). A vertex with fitness (a,b), with in-degree i and out-degree j, gains a new incoming edge with rate a(i+1) and an outgoing edge with rate b(j+1). The Bose-Einstein condensation occurs as a function of fitness distribution f(a,b).

Exact solution of a generalized model for surface deposition.

We consider a model for surface deposition in one dimension, in the presence of both precursor-layer diffusion and desorption. The model is a generalization that includes random sequential adsorption (RSA), accelerated RSA, and growth-and-coalescence models as special cases. Exact solutions are obtained for the model for both its lattice and continuum versions. Expressions are obtained for physically important quantities such as the surface coverage, average island size, mass-adsorption efficiency, and the process efficiency. The connection between a limiting case of the model and epidemic models is discussed.

Degree distributions of growing networks.

The in-degree and out-degree distributions of a growing network model are determined. The in-degree is the number of incoming links to a given node (and vice versa for out-degree). The network is built by (i) creation of new nodes which each immediately attach to a preexisting node, and (ii) creation of new links between preexisting nodes. This process naturally generates correlated in-degree and out-degree distributions. When the node and link creation rates are linear functions of node degree, these distributions exhibit distinct power-law forms. By tuning the parameters in these rates to reasonable values, exponents which agree with those of the web graph are obtained.

Measurement in vitro of pulsatile arterial diameter using a helium-neon laser.

A noncontacting in vitro measurement of pulsatile arterial diameter using a scanning optical micrometer is described. The major component of this system is a He-Ne laser whose beam scans the pulsating artery to be measured. The laser micrometer was integrated into a pulsatile perfusion apparatus that imposed various hemodynamic conditions on excised canine vessels. The laser system reliably tracked the pulsating arterial diameter at a particular longitudinal site as well as at various increments in the presence of an experimentally created stenosis. The He-Ne laser measured the radial motion of canine arteries and various vascular substitutes anastomosed in an end-to-end fashion. From these novel measurements, calculations were made of arterial compliance and bending stress, two biomechanical parameters that are implicated as potential causes of anastomotic intimal hyperplasia and graft failure. Although this device is inherently limited to in vitro use, it is a potentially useful instrument for vascular physiology and biophysics.