arXiv subjects

Yurij Holovatch

Publications and source records attributed to Yurij Holovatch.

At least 37 records · Page 2Linked to original sources

arXiv2021

Generalized Ising Model on a Scale-Free Network: An Interplay of Power Laws

We consider a recently introduced generalization of the Ising model in which individual spin strength can vary. The model is intended for analysis of ordering in systems comprising agents which, although matching in their binarity (i.e., maintaining the iconic Ising features of `+' or `$-$', `up' or `down', `yes' or `no'), differ in their strength. To investigate the interplay between variable properties of nodes and interactions between them, we study the model on a complex network where both the spin strength and degree distributions are governed by power laws. We show that in the annealed network approximation, thermodynamic functions of the model are self-averaging and we obtain an exact solution for the partition function. This allows us to derive the leading temperature and field dependencies of thermodynamic functions, their critical behavior, and logarithmic corrections at the interface of different phases. We find the delicate interplay of the two power laws leads to new universality classes.

cond-mat.stat-mech↗

arXiv2021

Extracting partition function zeros from Fukui-Todo simulations

The Fukui-Todo algorithm is an important element of the array of simulational approaches to tackling critical phenomena in statistical physics. The partition-function-zero approach is of fundamental importance to understanding such phenomena and a precise tool to measure their properties. However, because the Fukui-Todo algorithm bypasses sample-by-sample energy computation, zeros cannot easily be harnessed through the energy distribution. Here this obstacle is overcome by a novel reweighting technique and zero-detection protocol. The efficacy of the approach is demonstrated in simple iconic models which feature transitions of both first and second order.

cond-mat.stat-mech↗

arXiv2021

Network of scientific concepts: empirical analysis and modeling

Concepts in a certain domain of science are linked via intrinsic connections reflecting the structure of knowledge. To get a qualitative insight and a quantitative description of this structure, we perform empirical analysis and modeling of the network of scientific concepts in the domain of physics. To this end we use a collection of manuscripts submitted to the e-print repository arXiv and the vocabulary of scientific concepts collected via the ScienceWISE.info platform and construct a network of scientific concepts based on their co-occurrences in publications. The resulting complex network possesses a number of specific features (high node density, dissortativity, structural correlations, skewed node degree distribution) that can not be understood as a result of simple growth by several commonly used network models. We show that the model based on a simultaneous account of two factors, growth by blocks and preferential selection, gives an explanation of empirically observed properties of the concepts network.

cs.DL↗

arXiv2021

Variety of Scaling Laws for DNA Thermal Denaturation

We discuss possible mechanisms that may impact the order of the transition between denaturated and bound DNA states and lead to changes in the scaling laws that govern conformational properties of DNA strands. To this end, we re-consider the Poland-Scheraga model and apply a polymer field theory approach to calculate entropic exponents associated with the denaturated loop distribution. We discuss in particular variants of this transition that may occur due to the properties of the solution and may affect the self- and mutual interaction of both single and double strands. We find that the effects studied significantly influence the strength of the first order transition. This is manifest in particular by the changes in the scaling laws that govern DNA loop and strand distribution. As a quantitative measure of these changes we present the values of corresponding scaling exponents. For the $d=4-\varepsilon$ case we get corresponding $\varepsilon^4$ expansions and evaluate the perturbation theory expansions at space dimension $d=3$ by means of resummation technique.

cond-mat.soft↗

arXiv2020

Ising model with variable spin/agent strengths

We introduce varying spin strengths to the Ising model, a central pillar of statistical physics. With inhomogeneous physical systems in mind, but also anticipating interdisciplinary applications, we present the model on network structures of varying degrees of complexity. We solve it for the generic case of power-law spin strength and find that, with a self-averaging free energy, the model has a rich phase diagram with new universality classes. Indeed, the degree of complexity added by variable spins is on a par to that added by endowing simple networks with increasingly realistic geometries. It is suitable for modeling emergent phenomena in many-body systems in contexts where non-identicality of spins or agents plays an essential role and for exporting statistical physics concepts beyond physics.

cond-mat.stat-mech↗

arXiv2020

Large-scale structures in the $Λ$CDM Universe: network analysis and machine learning

We perform an analysis of the Cosmic Web as a complex network, which is built on a $Λ$CDM cosmological simulation. For each of nodes, which are in this case dark matter halos formed in the simulation, we compute 10 network metrics, which characterize the role and position of a node in the network. The relation of these metrics to topological affiliation of the halo, i.e. to the type of large scale structure, which it belongs to, is then investigated. In particular, the correlation coefficients between network metrics and topology classes are computed. We have applied different machine learning methods to test the predictive power of obtained network metrics and to check if one could use network analysis as a tool for establishing topology of the large scale structure of the Universe. Results of such predictions, combined in the confusion matrix, show that it is not possible to give a good prediction of the topology of Cosmic Web (score is $\approx$ 70 $\%$ in average) based only on coordinates and velocities of nodes (halos), yet network metrics can give a hint about the topological landscape of matter distribution.

astro-ph.CO↗

arXiv2020

Embedding technique and network analysis of scientific innovations emergence in an arXiv-based concept network

Novelty is an inherent part of innovations and discoveries. Such processes may be considered as an appearance of new ideas or as an emergence of atypical connections between the existing ones. The importance of such connections hints for investigation of innovations through network or graph representation in the space of ideas. In such representation, a graph node corresponds to the relevant concept (idea), whereas an edge between two nodes means that the corresponding concepts have been used in a common context. In this study we address the question about a possibility to identify the edges between existing concepts where the innovations may emerge. To this end, we use a well-documented scientific knowledge landscape of 1.2M arXiv.org manuscripts dated starting from April 2007 and until September 2019. We extract relevant concepts for them using the ScienceWISE.info platform. Combining approaches developed in complex networks science and graph embedding, we discuss the predictability of edges (links) on the scientific knowledge landscape where the innovations may appear.

cs.DL↗

arXiv2020

Possibility of a continuous phase transition in random-anisotropy magnets with ageneric random-axis distribution

We reconsider the problem of the critical behavior of a three-dimensional $O(m)$ symmetric magnetic system in the presence of random anisotropy disorder with a generic trimodal random axis distribution. By introducing $n$ replicas to average over disorder it can be coarse-grained to a $ϕ^{4}$-theory with $m \times n$ component order parameter and five coupling constants taken in the limit of $n \to 0$. Using a field theory approach we renormalize the model to two-loop order and calculate the $β$-functions within the $\varepsilon$ expansion and directly in three dimensions. We analyze the corresponding renormalization group flows with the help of the Padé-Borel resummation technique. We show that there is no stable fixed point accessible from physical initial conditions whose existence was argued in the previous studies. This may indicate an absence of a long-range ordered phase in the presence of random anisotropy disorder with a generic random axis distribution.

cond-mat.dis-nn↗

arXiv2019

Physicist's approach to public transportation networks: between data processing and statistical physics

In this paper we aim to demonstrate how physical perspective enriches usual statistical analysis when dealing with a complex system of many interacting agents of non-physical origin. To this end, we discuss analysis of urban public transportation networks viewed as complex systems. In such studies, a multi-disciplinary approach is applied by integrating methods in both data processing and statistical physics to investigate the correlation between public transportation network topological features and their operational stability. The studies incorporate concepts of coarse graining and clusterization, universality and scaling, stability and percolation behavior, diffusion and fractal analysis.

physics.soc-ph↗

arXiv2018

Data Mining in Scientometrics: usage analysis for academic publications

We perform a statistical analysis of scientific-publication data with a goal to provide quantitative analysis of scientific process. Such an investigation belongs to the newly established field of scientometrics: a branch of the general science of science that covers all quantitative methods to analyze science and research process. As a case study we consider download and citation statistics of the journal `Europhysics Letters' (EPL), as Europe's flagship letters journal of broad interest to the physics community. While citations are usually considered as an indicator of academic impact, downloads reflect rather the level of attractiveness or popularity of a publication. We discuss peculiarities of both processes and correlations between them.

cs.DL↗

arXiv2018

Bipartite graph analysis as an alternative to reveal clusterization in complex systems

We demonstrate how analysis of co-clustering in bipartite networks may be used as a bridge to connect, compare and complement clustering results about community structure in two different spaces: single-mode bipartite network projections. As a case study we consider scientific knowledge, which is represented as a complex bipartite network of articles and related concepts. Connecting clusters of articles and clusters of concepts via article-to-concept bipartite co-clustering, we demonstrate how concept features (e.g. subject classes) may be inferred from the article ones.

cs.DL↗

arXiv2018

Classical phase transitions in a one-dimensional short-range spin model induced by entropy depletion or complex fields

Ising's solution of a classical spin model famously demonstrated the absence of a positive-temperature phase transition in one-dimensional equilibrium systems with short-range interactions. No-go arguments established that the energy cost to insert domain walls in such systems is outweighed by entropy excess so that symmetry cannot be spontaneously broken. An archetypal way around the no-go theorems is to augment interaction energy by increasing the range of interaction. Here we introduce new ways around the no-go theorems by investigating entropy depletion instead. We implement this for the Potts model with invisible states.Because spins in such a state do not interact with their surroundings, they contribute to the entropy but not the interaction energy of the system. Reducing the number of invisible states to a negative value decreases the entropy by an amount sufficient to induce a positive-temperature classical phase transition. This approach is complementary to the long-range interaction mechanism. Alternatively, subjecting positive numbers of invisible states to imaginary or complex fields can trigger such a phase transition. We also discuss potential physical realisability of such systems.

cond-mat.stat-mech↗

arXiv2017

Exact solution of a classical short-range spin model with a phase transition in one dimension: the Potts model with invisible states

We present the exact solution of the 1D classical short-range Potts model with invisible states. Besides the $q$ states of the ordinary Potts model, this possesses $r$ additional states which contribute to the entropy, but not to the interaction energy. We determine the partition function, using the transfer-matrix method, in the general case of two ordering fields: $h_1$ acting on a visible state and $h_2$ on an invisible state. We analyse its zeros in the complex-temperature plane in the case that $h_1=0$. When ${\rm Im}\, h_2=0$ and $r\ge 0$, these zeros accumulate along a line that intersects the real temperature axis at the origin. This corresponds to the usual "phase transition" in a $1$D system. However, for ${\rm Im}\, h_2\neq 0$ or $r<0$, the line of zeros intersects the positive part of the real temperature axis, which signals the existence of a phase transition at non-zero temperature.

cond-mat.stat-mech↗

arXiv2017

Complex-network approach for visualizing and quantifying the evolution of a scientific topic

Tracing the evolution of specific topics is a subject area which belongs to the general problem of mapping the structure of scientific knowledge. Often bibliometric data bases are used to study the history of scientific topic evolution from its appearance to its extinction or merger with other topics. In this chapter the authors present an analysis of the academic response to the disaster that occurred in 1986 in Chornobyl (Chernobyl), Ukraine, considered as one of the most devastating nuclear power plant accidents in history. Using a bibliographic database the distributions of Chornobyl-related papers in different scientific fields are analysed, as are their growth rates and properties of co-authorship networks. Elements of descriptive statistics and tools of complex-network theory are used to highlight interdisciplinary as well as international effects. In particular, tools of complex-network science enable information visualization complemented by further quantitative analysis. A further goal of the chapter is to provide a simple pedagogical introduction to the application of complex-network analysis for visual data representation and interdisciplinary communication.

cs.DL↗

arXiv2017

The Fate of Ernst Ising and the Fate of his Model

On this, the occasion of the 20th anniversary of the "Ising Lectures" in Lviv (Ukraine), we give some personal reflections about the famous model that was suggested by Wilhelm Lenz for ferromagnetism in 1920 and solved in one dimension by his PhD student, Ernst Ising, in 1924. That work of Lenz and Ising marked the start of a scientific direction that, over nearly 100 years, delivered extraordinary successes in explaining collective behaviour in a vast variety of systems, both within and beyond the natural sciences. The broadness of the appeal of the Ising model is reflected in the variety of talks presented at the Ising lectures ( http://www.icmp.lviv.ua/ising/ ) over the past two decades but requires that we restrict this report to a small selection of topics. The paper starts with some personal memoirs of Thomas Ising (Ernst's son). We then discuss the history of the model, exact solutions, experimental realisations, and its extension to other fields.

physics.hist-ph↗

arXiv2017

Self-averaging in the random 2D Ising ferromagnet

We study sample-to-sample fluctuations in a critical two-dimensional Ising model with quenched random ferromagnetic couplings. Using replica calculations in the renormalization group framework we derive explicit expressions for the probability distribution function of the critical internal energy and for the specific heat fluctuations. It is shown that the disorder distribution of internal energies is Gaussian, and the typical sample-to-sample fluctuations as well as the average value scale with the system size $L$ like $\sim L \ln\ln(L)$. In contrast, the specific heat is shown to be self-averaging with a distribution function that tends to a $δ$-peak in the thermodynamic limit $L \to \infty$. While previously a lack of self-averaging was found for the free energy, we here obtain results for quantities that are directly measurable in simulations, and implications for measurements in the actual lattice system are discussed.

cond-mat.stat-mech↗

arXiv2016

Complex systems: physics beyond physics

Complex systems are characterized by specific time-dependent interactions among their many constituents. As a consequence they often manifest rich, non-trivial and unexpected behavior. Examples arise both in the physical and non-physical world. The study of complex systems forms a new interdisciplinary research area that cuts across physics, biology, ecology, economics, sociology, and the humanities. In this paper we review the essence of complex systems from a physicist's point of view, and try to clarify what makes them conceptually different from systems that are traditionally studied in physics. Our goal is to demonstrate how the dynamics of such systems may be conceptualized in quantitative and predictive terms by extending notions from statistical physics and how they can often be captured in a framework of co-evolving multiplex network structures. We mention three areas of complex-systems science that are currently studied extensively, the science of cities, dynamics of societies, and the representation of texts as evolutionary objects. We discuss why these areas form complex systems in the above sense. We argue that there exists plenty of new land for physicists to explore and that methodical and conceptual progress is needed most.

physics.soc-ph↗

arXiv2016

Universal shape characteristics for the mesoscopic polymer chain via dissipative particle dynamics

In this paper we study the shape characteristics of a polymer chain in a good solvent using a mesoscopic level of modelling. The dissipative particle dynamics simulations are performed in the $3D$ space at a range of chain lengths $N$. The scaling laws for the end-to-end distance and gyration radius are examined first and found to hold for $N\geq 10$ yielding reasonably accurate value for the Flory exponent $ν$. Within the same interval of chain lengths, the asphericity, prolateness, size ratio and other shape characteristics of the chain are found to become independent of $N$. Their mean values are found to agree reasonably well with the respective theoretical results and lattice Monte Carlo simulations. Broad probability distributions for the shape characteristics are found resembling in form the results of lattice Monte Carlo simulations. By means of analytic fitting of these distributions the most probable values for the shape characteristics are found to supplement their mean values.

cond-mat.soft↗