arXiv subjects

Piotr Fronczak

Publications and source records attributed to Piotr Fronczak.

At least 55 records · Page 3Linked to original sources

arXiv2010

Origins of Taylor's power law for fluctuation scaling in complex systems

Taylor's fluctuation scaling (FS) has been observed in many natural and man-made systems revealing an amazing universality of the law. Here we give strong theoretical foundations for the origins and abundance of Taylor's FS in different complex systems. The universality of our approach is validated against real world data ranging from bird and insect populations through human chromosomes and traffic intensity in transportation networks to stock market dynamics. Using fundamental principles of statistical physics (both equilibrium and non-equilibrium) we prove that Taylor's law results from the well-defined density of states (DOS) function of a system that gives the number of states characterized by the same value of a macroscopic parameter (i.e., the number of birds observed in a given area, traffic intensity measured as a number of cars passing trough a given observation point or daily activity in the stock market measured in millions of dollars).

physics.soc-ph↗

arXiv2009

Taylor's power law for fluctuation scaling in traffic

In this article, we study transportation network in Minnesota. We show that the system is characterized by Taylor's power law for fluctuation scaling with nontrivial values of the scaling exponent. We also show that the characteristic exponent does not unequivocally characterize a given road network, as it may differ within the same network if one takes into account location of observation points, season, period of day, or traffic intensity. The results are set against Taylor's fluctuation scaling in the Nagel-Schreckenberg cellular automaton model for traffic. It is shown that Taylor's law may serve, beside the fundamental diagram, as an indicator of different traffic phases (free flow, traffic jam etc.).

physics.soc-ph↗

arXiv2007

Critical line in undirected Kauffman boolean networks - the role of percolation

We show that to correctly describe the position of the critical line in the Kauffman random boolean networks one must take into account percolation phenomena underlying the process of damage spreading. For this reason, since the issue of percolation transition is much simpler in random undirected networks, than in the directed ones, we study the Kauffman model in undirected networks. We derive the mean field formula for the critical line in the giant component of these networks, and show that the critical line characterizing the whole network results from the fact that the ordered behavior of small clusters shields the chaotic behavior of the giant component. We also show a possible attitude towards the analytical description of the shielding effect. The theoretical derivations given in this paper quite tally with numerical simulations done for classical random graphs.

cond-mat.dis-nn↗

arXiv2007

Biased random walks on complex networks: the role of local navigation rules

We study the biased random walk process in random uncorrelated networks with arbitrary degree distributions. In our model, the bias is defined by the preferential transition probability, which, in recent years, has been commonly used to study efficiency of different routing protocols in communication networks. We derive exact expressions for the stationary occupation probability, and for the mean transit time between two nodes. The effect of the cyclic search on transit times is also explored. Results presented in this paper give the basis for theoretical treatment of the transport-related problems on complex networks, including quantitative estimation of the critical value of the packet generation rate.

cond-mat.dis-nn↗

arXiv2007

Kauffman Boolean model in undirected scale free networks

We investigate analytically and numerically the critical line in undirected random Boolean networks with arbitrary degree distributions, including scale-free topology of connections $P(k)\sim k^{-γ}$. We show that in infinite scale-free networks the transition between frozen and chaotic phase occurs for $3<γ< 3.5$. The observation is interesting for two reasons. First, since most of critical phenomena in scale-free networks reveal their non-trivial character for $γ<3$, the position of the critical line in Kauffman model seems to be an important exception from the rule. Second, since gene regulatory networks are characterized by scale-free topology with $γ<3$, the observation that in finite-size networks the mentioned transition moves towards smaller $γ$ is an argument for Kauffman model as a good starting point to model real systems. We also explain that the unattainability of the critical line in numerical simulations of classical random graphs is due to percolation phenomena.

cond-mat.dis-nn↗

arXiv2007

Thermodynamic forces, flows, and Onsager coefficients in complex networks

We present Onsager formalism applied to random networks with arbitrary degree distribution. Using the well-known methods of non-equilibrium thermodynamics we identify thermodynamic forces and their conjugated flows induced in networks as a result of single node degree perturbation. The forces and the flows can be understood as a response of the system to events, such as random removal of nodes or intentional attacks on them. Finally, we show that cross effects (such as thermodiffusion, or thermoelectric phenomena), in which one force may not only give rise to its own corresponding flow, but to many other flows, can be observed also in complex networks.

cond-mat.dis-nn↗

arXiv2007

Phase transitions in social networks

We study a model of network with clustering and desired node degree. The original purpose of the model was to describe optimal structures of scientific collaboration in the European Union. The model belongs to the family of exponential random graphs. We show by numerical simulations and analytical considerations how a very simple Hamiltonian can lead to surprisingly complicated and eventful phase diagram.

physics.soc-ph↗

arXiv2006

Log-periodic oscillations due to discrete effects in complex networks

We show that discretization of internode distribution in complex networks affects internode distances l_ij calculated as a function of degrees (k_i k_j) and an average path length as function of network size N. For dense networks there are log-periodic oscillations of above quantities. We present real-world examples of such a behavior as well as we derive analytical expressions and compare them to numerical simulations. We consider a simple case of network optimization problem, arguing that discrete effects can lead to a nontrivial solution.

cond-mat.dis-nn↗

arXiv2006

Publish or perish: analysis of scientific productivity using maximum entropy principle and fluctuation-dissipation theorem

Using data retrieved from the INSPEC database we have quantitatively discussed a few syndromes of the publish-or-perish phenomenon, including continuous growth of rate of scientific productivity, and continuously decreasing percentage of those scientists who stay in science for a long time. Making use of the maximum entropy principle and fluctuation-dissipation theorem, we have shown that the observed fat-tailed distributions of the total number of papers x authored by scientists may result from the density of states function g(x;τ) underlying scientific community. Although different generations of scientists are characterized by different productivity patterns, the function g(x;τ) is inherent to researchers of a given seniority τ, whereas the publish-or-perish phenomenon is caused only by an external field θinfluencing researchers.

physics.soc-ph↗

arXiv2006

Ferromagnetic fluid as a model of social impact

The paper proposes a new model of spin dynamics which can be treated as a model of sociological coupling between individuals. Our approach takes into account two different human features: gregariousness and individuality. We will show how they affect a psychological distance between individuals and how the distance changes the opinion formation in a social group. Apart from its sociological aplications the model displays the variety of other interesting phenomena like self-organizing ferromagnetic state or a second order phase transition and can be studied from different points of view, e.g. as a model of ferromagnetic fluid, complex evolving network or multiplicative random process.

physics.soc-ph↗

arXiv2005

Microscopic explanation of non-Debye relaxation for heat transfer

We give a microscopic explanation of both Debye and non-Debye thermalization processes that have been recently reported by Gall and Kutner (Physica A 352, 347 (2005)). Due to reduction of the problem to first passage phenomena we argue that relaxation functions f(t) introduced by the authors directly correspond to survival probabilities S(t) of particles in the considered systems. We show that in the case of broken ergodicity (i.e. in the case of mirror collisions) the survival probability decays as a power law S(t)=τ/t.

cond-mat.stat-mech↗

arXiv2005

Universal scaling of distances in complex networks

Universal scaling of distances between vertices of Erdos-Renyi random graphs, scale-free Barabasi-Albert models, science collaboration networks, biological networks, Internet Autonomous Systems and public transport networks are observed. A mean distance between two nodes of degrees k_i and k_j equals to =A-B log(k_i k_j). The scaling is valid over several decades. A simple theory for the appearance of this scaling is presented. Parameters A and B depend on the mean value of a node degree _nn calculated for the nearest neighbors and on network clustering coefficients.

cond-mat.dis-nn↗

arXiv2005

Fluctuation-dissipation relations for complex networks

In the paper, we study fluctuations over several ensembles of maximum-entropy random networks. We derive several fluctuation-dissipation relations characterizing susceptibilities of different networks to changes in external fields. In the case of networks with a given degree sequence, we argue that the scale-free topologies of real-world networks may arise as a result of self-organization of real systems into sparse structures with low susceptibility to random external perturbations. We also show that the ensembles of networks with noninteracting links (both uncorrelated and with two-point correlations) are equivalent to random networks with hidden variables.

cond-mat.dis-nn↗

arXiv2005

Interplay between network structure and self-organized criticality

We investigate, by numerical simulations, how the avalanche dynamics of the Bak-Tang-Wiesenfeld (BTW) sandpile model can induce emergence of scale-free (SF) networks and how this emerging structure affects dynamics of the system. We also discuss how the observed phenomenon can be used to explain evolution of scientific collaboration.

cond-mat.other↗

arXiv2005

Networks with given two-point correlations: hidden correlations from degree correlations

The paper orders certain important issues related to both uncorrelated and correlated networks with hidden variables. In particular, we show that networks being uncorrelated at the hidden level are also lacking in correlations between node degrees. The observation supported by the depoissonization idea allows to extract distribution of hidden variables from a given node degree distribution. It completes the algorithm for generating uncorrelated networks that was suggested by other authors. In this paper we also carefully analyze the interplay between hidden attributes and node degrees. We show how to extract hidden correlations from degree correlations. Our derivations provide mathematical background for the algorithm for generating correlated networks that was proposed by Boguna and Pastor-Satorras.

cond-mat.dis-nn↗

arXiv2005

How to calculate the main characteristics of random uncorrelated networks

We present an analytic formalism describing structural properties of random uncorrelated networks with arbitrary degree distributions. The formalism allows to calculate the main network characteristics like: the position of the phase transition at which a giant component first forms, the mean component size below the phase transition, the size of the giant component and the average path length above the phase transition. We apply the approach to classical random graphs of Erdos and Renyi, single-scale networks with exponential degree distributions and scale-free networks with arbitrary scaling exponents and structural cut-offs. In all the cases we obtain a very good agreement between results of numerical simulations and our analytical predictions.

cond-mat.dis-nn↗

arXiv2004

Average path length in random networks

Analytic solution for the average path length in a large class of random graphs is found. We apply the approach to classical random graphs of Erdös and Rényi (ER) and to scale-free networks of Barabási and Albert (BA). In both cases our results confirm previous observations: small world behavior in classical random graphs $l_{ER} \sim \ln N$ and ultra small world effect characterizing scale-free BA networks $l_{BA} \sim \ln N/\ln\ln N$. In the case of scale-free random graphs with power law degree distributions we observed the saturation of the average path length in the limit of $N\to\infty$ for systems with the scaling exponent $2< α<3$ and the small-world behaviour for systems with $α>3$.

cond-mat.dis-nn↗

arXiv2003

How to calculate the main characteristics of random graphs - a new approach

The poster presents an analytic formalism describing metric properties of undirected random graphs with arbitrary degree distributions and statistically uncorrelated (i.e. randomly connected) vertices. The formalism allows to calculate the main network characteristics like: the position of the phase transition at which a giant component first forms, the mean component size below the phase transition, the size of the giant component and the average path length above the phase transition. Although most of the enumerated properties were previously calculated by means of generating functions, we think that our derivations are conceptually much simpler.

cond-mat.stat-mech↗