Theory Of Scale-free Networks Research Articles

Study on Cascading Failure and Elasticity of UAV Swarm Communication Network

As an important new force in information warfare, the UAV swarm has attracted more and more attention from militarists and scholars of various countries. At present, large-scale and low-cost UAV swarms have been gradually applied to reconnaissance operations. The invulnerability and elasticity of its communication network are the key factors that determine whether the swarm can successfully complete the reconnaissance mission. The research object of this study is the UAV swarm communication network, which performs reconnaissance missions. The swarm network is extracted as a three-layer interdependent network model. We apply scale-free network theory and the new connection probability equation to establish three subnetworks. Then, a three-layer interdependent network is constructed by using the interlayer connection rules of the nodes with the largest degree. The classical M-L model is extended to an interdependent network. An interlayer load redistribution rule is proposed to simulate the cascading failure process of swarm networks. Then, we attack network nodes randomly and observe the invulnerability and elasticity of this network. The simulation results show that the performance of the interdependent network is better than that of the single-layer network due to the effect of the interdependent links. By analyzing the experimental results, some feasible suggestions are put forward to improve the invulnerability and elasticity of reconnaissance UAV swarm communication network.

Age of Information Modeling and Optimization for Fast Information Dissemination in Vehicular Social Networks

Autonomous vehicles (AVs) with advanced communication, computing, and control capabilities will provide not only a convenient means of transportation but also an emerging platform for real-time social communications and networking. Thereby, it is crucial to enable timely exchange of information over the dynamic cyber-physical-social system enabled by the AVs. In this paper, we consider age of information (AoI)-centric real-time information dissemination over vehicular social networks (VSNs), where the AoI captures the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">freshness</i> of received data packets. We first propose a mathematical framework based on the mean-field theory (MFT) to analyze the network AoI (NAoI) of VSNs, namely AoI for the AV that lastly receives the information update in the network. The proposed analytical framework considers both the social features of vehicular networks, which are characterized using the scale-free network theory, and the underlying wireless communication process to evaluate the NAoI. Then, we further consider joint optimization of the information update rate at the source node and the transmit probabilities at the AVs for minimization of the average peak NAoI (PNAoI), i.e., the time average of peak values occurred in the evolution of the NAoI, which is solved via a novel parametric optimization scheme. Both analytical and simulation results show that compared with several baseline schemes, the proposed scheme can exploit the scale-free feature of the VSNs to significantly lower the average PNAoI by up to 96 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

Probability-based Vendor Selection Model for the Hungarian Automotive Supply Network

The aim of this paper is to investigate the structure of the Hungarian automotive supply network and provide a possible solution that mathematically describes the connections between the interested parties. In the study an approximate model is introduced to determine the links between hubs (car manufacturers), nodes (Tier1 suppliers) and edges, combining probability random graph and scale free network theory. During the simulation some main drivers were applied for selection purposes, such as location, turnover, product profile. As a result of the study a potential tool has been designed to support decision-making.

Identification of common points in hybrid geodetic networks to determine vertical movements of the Earth’s crust

Abstract Simultaneous use of data repeated levelling measurements and continuous GNSS observations allows increasing the spatial resolution of geodynamics models. For this purpose, it is necessary to create a single network, a so-called hybrid network. This paper aims at examining the possibility of using scale-free network theory to determine the most relevant common points in hybrid networks using the distance criterion. Used on European network points: UELN (United European Levelling Network) and EPN (European Permanent GPS Network) and the regional network. In the hybrid network (UELN + EPN), 18 pseudo-nodal points with the highest number of links were identified. The accepted distance criterion shows that about 90 % of the EPN points can be used as common points. The application of the scale-free network theory allows determining the significance of points in a hybrid network.

Quantum-Like Interdependence Theory Advances Autonomous Human-Machine Teams (A-HMTs).

As humanity grapples with the concept of autonomy for human–machine teams (A-HMTs), unresolved is the necessity for the control of autonomy that instills trust. For non-autonomous systems in states with a high degree of certainty, rational approaches exist to solve, model or control stable interactions; e.g., game theory, scale-free network theory, multi-agent systems, drone swarms. As an example, guided by artificial intelligence (AI, including machine learning, ML) or by human operators, swarms of drones have made spectacular gains in applications too numerous to list (e.g., crop management; mapping, surveillance and fire-fighting systems; weapon systems). But under states of uncertainty or where conflict exists, rational models fail, exactly where interdependence theory thrives. Large, coupled physical or information systems can also experience synergism or dysergism from interdependence. Synergistically, the best human teams are not only highly interdependent, but they also exploit interdependence to reduce uncertainty, the focus of this work-in-progress and roadmap. We have long argued that interdependence is fundamental to human autonomy in teams. But for A-HMTs, no mathematics exists to build from rational theory or social science for their design nor safe or effective operation, a severe weakness. Compared to the rational and traditional social theory, we hope to advance interdependence theory first by mapping similarities between quantum theory and our prior findings; e.g., to maintain interdependence, we previously established that boundaries reduce dysergic effects to allow teams to function (akin to blocking interference to prevent quantum decoherence). Second, we extend our prior findings with case studies to predict with interdependence theory that as uncertainty increases in non-factorable situations for humans, the duality in two-sided beliefs serves debaters who explore alternatives with tradeoffs in the search for the best path going forward. Third, applied to autonomous teams, we conclude that a machine in an A-HMT must be able to express itself to its human teammates in causal language however imperfectly.

Theory of Scale-Free Networks as a New Tool in Researching the Structure and Optimization of Spatial Planning

AbstractThe problems covered by this article refer to the possibility of connecting Barabasi’s theory of scale-free networks with the concept of growth poles and the theory of spatial self-organiza...

A safe space of rural areas in the context of the occurrence of extreme weather events—A case study covering a part of the Euroregion Baltic

Ongoing climate change was first noticed by the public a few decades ago. The factual occurrence of such a change and its impact on the space were confirmed by both results of scientific research and economic assessments carried out for a variety of economic purposes. The economic sectors whose activities are based on natural weather conditions are most vulnerable to the effects of ongoing climate change. An increase in the number of extreme events, and primarily of their effects, are deeply felt by, primarily, the agricultural community. The study focused on the agricultural sector involved in crop cultivation within a part of the Euroregion Baltic. For analyses, statements on financial losses in agriculture, suffered in the years 2010–2014 and caused by extreme weather events (inter alia rainstorm, hail, cyclones, lightning strike, flood, spring frost, drought, the negative effects of over-wintering of crops), were used. By applying the theory of scale-free networks, a network of relationships was constructed, which allowed the authors to indicate sensitive places vulnerable to financial consequences of such events in rural areas.The European Union policy which also embraces the fragment of the part of the Euroregion Baltic under study aims at broadening the range of policy instruments which are to affect changes to land use patterns in order to find the balance between the outcomes of agricultural production and the adverse effects brought about by this production to natural ecosystems. This direction of activities is also aimed at preventing the effects of climate change. The constructed scale-free network model indicated that not all agricultural areas are equally affected by the financial effects of the occurrence of extreme events. An analysis enabled the indication of the most vulnerable (sensitive) locations, the so-called hubs, while the investigated topology of the system leads to the conclusion that financial losses in the current sensitive locations may increase to an unimaginable level. Knowledge of the nature of the system, however, allows one to affect its evolution. Targeting of the European Union policy instruments toward the areas most affected by the consequences of extreme weather events will make agricultural producers less vulnerable to them and strengthen the farmer community’s trust in the expected financial results of the farm work.

INVESTIGATION OF SCALE-INVARIANT PROPERTY OF ORGANIZATION SYSTEM OF TRAIN TRAFFIC VOLUME BASED ON THE PERCOLATION THEORY

Purpose. The work is devoted to the study the property of scaling invariance of the organization system of train traffic volume on Ukrainian railways. Methodology. To prove the real network origin of Trains Formation Plan (TFP) destination to the type of socalled scale-invariant networks it is proposed to generate scale-free networks with different dimensions, Barabási–Albert type with parameters that real networks of TFP destination has and to investigate their structure on survivability using the procedure of percolation nodes. Percolation process is proposed to be considered as a modified version of the spatial movement of cars on the network by increasing the number of railway stations, which have lost the ability to perform the basic function to pass cars on TFP destination in terms of adverse effects (an accident, overload). Findings. Comparative analysis of percolation at random and targeted destructive impact on network nodes has shown matching with the results of real network percolation of TFP destination, which proves the existence of self-similarity. Comparable figures in percolation were: percentage of remote stations in the network, in which the network fragmentation occurs, the average inverse path between network nodes, the diameter of the graph structure, the size meaning of the second largest cluster in the network from the steps of destruction. Originality. For the first time the hypothesis of the existence of scaling invariance properties of the graph TFP destinations on the railways of Ukraine, which can be attributed to a class of the graph scale-free networks was confirmed. Existing knowledge in the field theory of scale-free networks can be used to describe the survivability of system transportation on the railways of Ukraine. Practical value. Based on the identified properties of system directions of train traffic volumes, it is possible to create a mathematical model in the future that will predict the behavior of the transportation system with network structure. Properties analysis of system survivability of train traffic volumes will optimize the use of capital investments to increase network capacity by identifying the most critical lines and stations that systematically affect the efficiency of the network as a whole.

Reply to comments on “Surname distribution in population genetics and in statistical physics”

I can only agree with almost all the observations made in the Comments, and I will only stress some points complementing my review or suggesting new promising lines of investigation. I will also quote the most relevant recent contributions, missing in the review and suggested by the comments. Baek and Kim correctly point out the heterogeneity of surname distributions, and suggest that in general scaling laws must be seen as the most random outcome under certain constraints; hence the analysis should focus on the constraints and not on the specific dynamics [1]. In the RG approach this would correspond to focussing on the relevant field operator associated with the introduction of new surnames. Boattini and Pettner offer a convincing explaination of the relative decline of biodemography in the last decade (based especially on the advent of easier end cheaper technologies for DNA typing, and partially on the difficulties raised by polyphyletism). However they stress that there is still much interest in comparing surnames and Y-chromosome haplotypes and indicate the existence of several possible directions for a fruitful interdisciplinary approach (among them anthropology, forensics [2], genetic genealogy [3] and especially linguistics [4]). Maruvka and Shnerb underline the connection of the statistical approach to surname studies with many other developments, not only in population dymamics, but also in the theory of scale-free networks. However they raise a number of technical problems that should be the object of further analysis, among them the limiting assumptions of fixed size or fixed growth rate of the population, and the difficult comparison of theoretical models with empirical data generated by sampling [5]. As a side comment I would like to stress that the application of surname statistics to the study of nepotism in the academia has been the object also of one of the references quoted in my review [6]. Also Manrubia stresses the importance of shifting the attention from the commonalities to the dissimilarities. She also underlines the relevance of surname patterns to solve questions in history and anthropology (migratory phenomena, transmission of cultural traits) and suggests the possibility of deriving a ”surname phylogenies” in analogy with linguistic phylogenies, and exploring second and higher order contributions to the composition of human groups. Zanette emphasizes the problems raised by polyphyletism, and suggests an extensive analysis of this aspect of surname dynamics, as well as a better exploration of the mechanisms of surname mutation, possibly in connection with the study of linguistic evolution. He also stresses the lack of a rigorous analysis of the mutual compatibility of the theoretical methods applied to the study of surname dynamics. To this purpose I would like to notice that one of the aims of my review was to show that many of the results presented in the literature are substantially equivalent since they basically derive from different representations of the same Yule-Simon process.

Economics of information security investment in the case of concurrent heterogeneous attacks with budget constraints

In this study we develop an analytic model for information security investment allocation of a fixed budget. Our model considers concurrent heterogeneous attacks with distinct characteristics and derives the breach probability functions based on the theory of scale-free networks. The relationships among the major variables, such as network exposure, potential loss due to a security breach, investment effectiveness, and security investment levels, are investigated via analytical and numerical analyses subject to various boundary conditions. In particular, our model shows how a firm should allocate its limited information security budget to defend against two classes of security attacks (targeted and opportunistic) concurrently. Among the results of these analyses, we find that a firm with a limited security budget is better off allocating most or all of the investment to measures against one of the classes of attack. Further, we find that managers should focus the security investment on preventing targeted attacks when the information systems are highly connected and relatively open and when the potential loss is large relative to the security budget.

Terminology and the theory of scale-free networks

Summary The goal of these studies is to draw attention to the application of the theory of scale-free networks in terminology. In the last few years there have been a lot of publications on the application of the scale-free network model (for a summary see Barabási 2002 and Csermely 2006). A brief summary of the basic information regarding networks will be provided and previous findings of linguistic research that were interpreted with the help of network models (terminology, quantitative linguistics, generative linguistics, psycholinguistics) will be surveyed. Before networks were widely researched, there were a great number of linguistic findings on natural languages that now lend themselves to new interpretation through network theory. In the paper calculations on the numeric data obtained from previous publications on quantitative linguistic research will be demonstrated. Finally, I will elaborate on some aspects of the application of network theory in terminology, and introduce the model of terminological networks.

CHARACTERISTICS OF CLASS COLLABORATION NETWORKS IN LARGE JAVA SOFTWARE PROJECTS

Understanding software structural complexity and evolution plays an important role in controlling the software development and maintenance process. Recent studies have shown that the theory behind complex networks, especially the theory of scale-free networks, can be a useful approach to the analysis of concrete software systems. In this paper, class collaboration networks associated with five large Java software systems (JDK, Ant, Tomcat, Lucene and JavaCC) are analyzed in order to determine whether they belong to the class of scale-free networks, and examine their small-world characteristics. For each analyzed network, we detected (approximately) scale-free and (ultra) small-world properties. The results indicate that general conclusions from scale-free network theory can be applied to Java software systems in order to understand their structural complexity and model software evolution at the structural (class collaboration) level. Moreover, we examine class collaboration network evolution of Ant, in order to check the preferential attachment hypothesis of the Barabasi-Albert model. For several major Ant network transitions we con-clude that preferential attachment can successfully model Ant evolution at the class collaboration level. Finally, we discuss the implications of our results on software engineering, in several aspects: identification of important clas-ses/interfaces, software testing strategy, and efficient communication among software entities.http://dx.doi.org/10.5755/j01.itc.40.1.192

Supply network topology and robustness against disruptions – an investigation using multi-agent model

In this study we examine the relationship between supply network's topology and its robustness in the presence of random failures and targeted attacks. The agent-based model developed in this paper uses the basic framework and parameters in the experimental game presented in Sterman [1989, Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making context. Management Science, 35 (3), 321–339] for modelling adaptive managerial decision making in an inventory management context. The study extends the linear supply chain context to a complex supply network and undertakes a rigorous examination of robustness of these supply networks that are characterised by distinct network characteristics. We theorise that network characteristics such as average path length, clustering coefficient, size of the largest connected component in the network and the maximum distance between nodes in the largest connected component are related to the robustness of supply networks, and test the research hypotheses using data from several simulation runs. Simulations were carried out using 20 distinct network topologies where 10 of these topologies were generated using preferential attachment approach (based on the theory of scale-free networks) and the remaining 10 topologies were generated using random attachment approach (using random graph theory as a foundation). These 20 supply networks were subjected to random demand and their performances were evaluated by considering varying probabilities of random failures of nodes and targeted attacks on nodes. We also consider the severity of these disruptions by considering the downtime of the affected nodes. Using the data collected from a series of simulation experiments, we test the research hypotheses by means of binomial logistic regression analysis. The results point towards a significant association between network characteristics and supply network robustness assessed using multiple performance measures. We discuss the implications of the study and present directions for future research.

A Simulation Approach To The Control Mechanism Of Individual And Web Site In Malware Spread

Abstract Malware has become the major threat to security and health of computers and Internet. The multi-agent simulation model of malware spreading is designed based on the Scale-Free network theory, which focuses on the control of its spread. Through simulation experiments, it is found that the speed of malware spreading is inhibited with the increasing of long-lasting immunity node, and is accelerated with that of the infection rate. It is also found that the (learning) ability plays a decisive role on the control of malware spreading and the average connection has important impact on the speed of spreading. The results show that in order to control the spread of malware, intemet users should enhance awareness of malware and solving ability, while the government should strengthen the supervision on the Internet and promote the development of security software through effective measures.

An Improved Wireless Sensor Network Mode based on Complex Network Theory

This paper proposes an improved scale-free network model this paper introduces the traditional scale-free network theory and analysis methods of more appropriate wireless sensor network, it will be more close to the real wireless sensor network statement. The improved model is based on the complex network theory prove research more suitable to the wireless sensor network.

A BRIEF INTRODUCTION TO SCALE-FREE NETWORKS

. This article provides a brief introduction to scale-free networks. The notion of a scale-free network is defined and some examples given. Properties frequently exhibited by scale-free networks are discussed. The importance of the phenomenon of preferential attachment in generating scale-free networks is illustrated with two examples for the spread of a persistent disease. The models are similar in that they both yield a total infected population (1) which is geometrically distributed, and growing exponentially in expectation; and (2) in which the average distance from the original source of infection grows in a similar way over time. However one model, which has preferential attachment (infection), yields a scale-free network, while the other which has homogeneous infectivity does not. The possible application of the theory of scale-free networks to resource management is briefly discussed.

Congruent epidemic models for unstructured and structured populations: Analytical reconstruction of a 2003 SARS outbreak

Both the threat of bioterrorism and the natural emergence of contagious diseases underscore the importance of quantitatively understanding disease transmission in structured human populations. Over the last few years, researchers have advanced the mathematical theory of scale-free networks and used such theoretical advancements in pilot epidemic models. Scale-free contact networks are particularly interesting in the realm of mathematical epidemiology, primarily because these networks may allow meaningfully structured populations to be incorporated in epidemic models at moderate or intermediate levels of complexity. Moreover, a scale-free contact network with node degree correlation is in accord with the well-known preferred mixing concept. The present author describes a semi-empirical and deterministic epidemic modeling approach that (a) focuses on time-varying rates of disease transmission in both unstructured and structured populations and (b) employs probability density functions to characterize disease progression and outbreak controls. Given an epidemic curve for a historical outbreak, this modeling approach calls for Monte Carlo calculations (that define the average new infection rate) and solutions to integro-differential equations (that describe outbreak dynamics in an aggregate population or across all network connectivity classes). Numerical results are obtained for the 2003 SARS outbreak in Taiwan and the dynamical implications of time-varying transmission rates and scale-free contact networks are discussed in some detail.

A paradigm for viewing biologic systems as scale-free networks based on energy efficiency: Implications for present therapies and the future of evolution

A network constitutes an abstract description of the relationships among entities, respectively termed links and nodes. If a power law describes the probability distribution of the number of links per node, the network is said to be scale-free. Scale-free networks feature link clustering around certain hubs based on preferential attachments that emerge due either to merit or legacy. Biologic systems ranging from sub-atomic to ecosystems represent scale-free networks in which energy efficiency forms the basis of preferential attachments. This paradigm engenders a novel scale-free network theory of evolution based on energy efficiency. As environmental flux induces fitness dislocations and compels a new meritocracy, new merit-based hubs emerge, previously merit-based hubs become legacy hubs, and network recalibration occurs to achieve system optimization. To date, Darwinian evolution, characterized by innovation sampling, variation, and selection through filtered termination, has enabled biologic progress through optimization of energy efficiency. However, as humans remodel their environment, increasing the level of unanticipated fitness dislocations and inducing evolutionary stress, the tendency of networks to exhibit inertia and retain legacy hubs engender maladaptations. Many modern diseases may fundamentally derive from these evolutionary displacements. Death itself may constitute a programmed adaptation, terminating individuals who represent legacy hubs and recalibrating the network. As memes replace genes as the basis of innovation, death itself has become a legacy hub. Post-Darwinian evolution may favor indefinite persistence to optimize energy efficiency. We describe strategies to reprogram or decommission legacy hubs that participate in human disease and death.

Scale-free networks versus evolutionary drift

Recent studies of properties of various biological networks revealed that many of them display scale-free characteristics. Since the theory of scale-free networks is applicable to evolving networks, one can hope that it provides not only a model of a biological network in its current state but also sheds some insight into the evolution of the network. In this work, we investigate the probability distributions and scaling properties underlying some models for biological networks and protein domain evolution. The analysis of evolutionary models for domain similarity networks indicates that models which include evolutionary drift are typically not scale free. Instead they adhere quite closely to the Yule distribution. This finding indicates that the direct applicability of scale-free models in understanding the evolution of biological network may not be as wide as it has been hoped for.

Plutarch in the Galaxy of New Media. Mechanisms of Reception

Plutarch in the Galaxy of New Media. Mechanisms of Reception&#x0D; The aim of this article is to present the most important mechanisms of appearance Plutarch’s texts on the Internet. The author, referring to the scale-free network theory, identifies three overlapping each other spaces of reception: ancient literature, literature and popular culture, new media. The last one represents in the frame of Lev Manovich, whose book The language of new media complements Henry Jenkins in the concept of Convergence Culture.