Evolution Of Network Structure Research Articles

Social climber attachment in forming networks produces a phase transition in a measure of connectivity

The formation and fragmentation of networks are typically studied using percolation theory, but most previous research has been restricted to studying a phase transition in cluster size, examining the emergence of a giant component. This approach does not study the effects of evolving network structure on dynamics that occur at the nodes, such as the synchronization of oscillators and the spread of information, epidemics, and neuronal excitations. We introduce and analyze an alternative link-formation rule, called social climber (SC) attachment, that may be combined with arbitrary percolation models to produce a phase transition using the largest eigenvalue of the network adjacency matrix as the order parameter. This eigenvalue is significant in the analyses of many network-coupled dynamical systems in which it measures the quality of global coupling and is hence a natural measure of connectivity. We highlight the important self-organized properties of SC attachment and discuss implications for controlling dynamics on networks.

Accelerating consensus on coevolving networks: The effect of committed individuals

Social networks are not static but, rather, constantly evolve in time. One of the elements thought to drive the evolution of social network structure is homophily-the need for individuals to connect with others who are similar to them. In this paper, we study how the spread of a new opinion, idea, or behavior on such a homophily-driven social network is affected by the changing network structure. In particular, using simulations, we study a variant of the Axelrod model on a network with a homophily-driven rewiring rule imposed. First, we find that the presence of rewiring within the network, in general, impedes the reaching of consensus in opinion, as the time to reach consensus diverges exponentially with network size N. We then investigate whether the introduction of committed individuals who are rigid in their opinion on a particular issue can speed up the convergence to consensus on that issue. We demonstrate that as committed agents are added, beyond a critical value of the committed fraction, the consensus time growth becomes logarithmic in network size N. Furthermore, we show that slight changes in the interaction rule can produce strikingly different results in the scaling behavior of consensus time, T(c). However, the benefit gained by introducing committed agents is qualitatively preserved across all the interaction rules we consider.

Evolving artificial neural network structure using grammar encoding and colonial competitive algorithm

Evolving artificial neural network usually refers to network structure evolution leaving the network’s parameters to be trained using conventional algorithms. In this paper, we present a new method for artificial neural network evolution that evolves the network structure along with the network parameters. The proposed method uses grammatical encoding together with colonial competitive algorithm to evolve artificial neural network structure and parameters. This allows for a better description of the network using a formal grammar allowing the network architecture to shape the resulting search space in order to meet each problem requirement. The proposed method is compared with other five methods for artificial neural network training and is evaluated using four known regression problems. In all four datasets, the proposed method outperforms its competitors.

Trust and Treason: Social Network Structure as a Source of Flexibility in Peace Negotiations

AbstractTo reach a negotiated peace settlement, the parties to the conflict have to show flexibility in their negotiation positions. This article examines why some rebel groups are flexible on the main issue of contention, whereas others are not. The study explores rebels’ social network structure as a source of negotiation flexibility. The proposition that a rebel group structured as a “trust network” can manage the risks of peace making through a “trust mechanism” of information sharing, verification, and mutual influence between rebel negotiators and non‐negotiating rebel leaders, is consistent with a procedural justice logic. The proposition is analyzed for negotiations between LTTE and the Sri Lankan government, and Gerakan Aceh Merdeka (GAM) and the Indonesian government. The findings support the proposition, and highlight the potential of social network analysis to further the understanding of conflict resolution. Policy wise, the evolution of network structure implies that the likelihood of negotiation success varies over time.

Structural characterization of gamma irradiated lithium phosphate glasses containing variable amounts of molybdenum

MoO 3-doped lithium phosphate glasses with varying molybdenum contents were prepared and characterized. Combined UV–visible and infrared (FT/IR) spectroscopic studies were measured before and after gamma irradiation. Experimental results indicate that molybdenum ions occupy different local sites including presence of Mo 3+, Mo 4+ and Mo 5+ with different ratio depending on the host lithium phosphate glass composition and concentration of molybdenum ions. Undoped lithium phosphate glass reveal UV absorption which is related to the presence of unavoidable trace iron impurities. FT/IR spectra were used to identify the main building structural groups present in the studied lithium phosphate glass. The same spectroscopic reutilized techniques were reutilized to identify the induced defects generated after successive gamma irradiation and the influence of molybdenum ions on the irradiation process. Experimental results indicate that molybdenum ions occupy two valences: Mo 5+ and Mo 3+ with the first in dominant percent. The change in the UV, visible and infrared spectral data are discussed in relation to the change in the state of molybdenum ions with glass composition or dopant concentration or network structure evolution caused by the change in glass constituents.

Rheo-SANS Studies on Shear Thickening in Clay−Poly(ethylene oxide) Mixed Solutions

The structure evolution of network structure in clay−polymer aqueous solutions after shear thickening under shear deformation was investigated by means of small-angle neutron scattering (SANS; Rheo-SANS). Laponite (clay; C) platelet dispersions in poly(ethylene oxide) (PEO; P) aqueous solutions with various deuterious/hydrogeneous water compositions were prepared, and contrast-variation SANS experiments were carried out under flow field. The two-dimensional scattering contour patterns changed suddenly from isotropic to anisotropic patterns at the shear thickening transition point. The orientations of clay platelets and PEO chains were individually characterized by the partial structure factors, SCC(q), SCP(q), and SPP(q), which were obtained by decomposition of the SANS intensity functions. The SANS results show that PEO chains are adsorbed on clay platelets even in a flow field and are peeled off at the shear thickening transition point. Flow birefringence results suggest that the shear thickening is a c...

Prisoner's Dilemma on Co-Evolving Networks without Strategy Update

We investigate the game theory in a structured population with the assumption that the evolution of network structure is far faster than that of strategy update. We find that the degree distribution for the final network consists of two distinct parts: the low degree part which is contributed to by defectors and a broadband in the regime with high degree which is formed by cooperators. The structure of the final network and the final strategy pattern have also been numerically proved to be independent of the game parameters.

A rewriting grammar for heat exchanger network structure evolution with stream splitting

The design of cost optimal heat exchanger networks is a difficult optimization problem owing both to the nonlinear models required and the combinatorial size of the search space. When stream splitting is considered, the combinatorial aspects make the problem even harder. This article describes the implementation of a two-level evolutionary algorithm based on a string rewriting grammar for the evolution of the heat exchanger network structure. A biological analogue of genotypes and phenotypes is used to describe structures and specific solutions, respectively. The top-level algorithm evolves structures while the lower level optimizes specific structures. The result is a hybrid optimization procedure that can identify the best structures including stream splitting. Case studies from the literature are presented to demonstrate the capabilities of the novel procedure.

Encapsulation of Protein in Silica Matrices: Structural Evolution on the Molecular and Nanoscales

The immobilization of biological species such as proteins and enzymes in sol-gel hosts is currently an area of intense research activity. However, the majority of these studies have been directed toward investigating the biological activity or physicochemical properties of the encapsulated species, with much less attention having been directed toward the effect of proteins on the structural evolution of the sol-gel matrix. This study investigates the structural evolution of sol-gel matrices in the presence of a model protein, bovine serum albumin (BSA). The sol-gel matrices were produced via the NaF-catalyzed hydrolysis of a mixture of tetramethyoxysilane (TMOS) and methyltrimethoxysilane (MTMS), yielding nanohybrid matrices with controlled pore sizes, pore volumes, and surface chemistry. The structural evolution of the matrix was investigated using a complementary suite of techniques, including solid-state (29)Si NMR, FTIR, SANS contrast variation, and N(2) sorption. A novel approach was developed to model the SANS data, to extract key structural parameters. The results indicated that the structural evolution of the matrices was modulated by a series of complex interactions between the enzyme and the evolving sol-gel nanohybrid: On the molecular scale, increasing BSA content led to an associated increase in both the abundance of linear Si-O-Si species (FTIR) and the Qn network connectivity ((29)Si NMR). However, only minor changes in the connectivity of the evolving Tn network were evident with varying BSA content. The selective role of the protein in these systems, where the approach of the methylated monomer to the vicinity of the protein's surface is presumably impeded by the hydrophobicity of the monomer, will be discussed. On the nanoscale, N(2) sorption data were consistent with an initial increase in the mesopore volume and surface area at low BSA loadings, followed by a subsequent monotonic decrease with increasing BSA content. In contrast, no such trends were evident in the in situ SANS data obtained from these samples, suggesting that modulation of the evolving network structure of the silica matrix by BSA during condensation prevents collapse of the nanoscale gel structure during freeze-drying. This latter comparison reflects the important role of in situ techniques such as small angle scattering (which can be used to study both open and closed porosity and probe nanostructure on length scales from approximately 1 nm to >100 nm) in investigating such complex, multicomponent systems, and techniques for modeling such data in sol-gel systems will be discussed.

Modeling the Inheritance and Variation based on the Fractal for Techno-Innovation Network Structure: From Viewpoint of Integrating Time with Hierarchy

Abstract Starting from space-time relationship, an evolution model for techno-innovation network structure integrating time process with hierarchical decomposition is constructed, in which a qualitative analysis of inheritance and variation characteristics of the network structure in different observing scales by utilizing variations in time scales is made, and the theory basis of techno-innovation network structure evolution is put out in methodology. The findings are as follows: 1) With variations in observing scale, either for the evolution or for the hierarchy of techno-innovation network structure, there is a relation to some degree of as one falls the other rises between inheritance and variation characteristics. 2) The connections between hierarchies of techno-innovation network structure are in essence relations between the individuals and the collectivity, which are indefinite relations, and the variation is always in existence. 3) The theoretical model can be built through analysis of time series data, which is achieved by adopting rescaled range analysis (R/S analysis).

Effect of mortality selection on the emergence of cooperation with network dynamics

Most previous studies have focused on the effect of fertility selection on the evolution of cooperation. In fact, the payoff to an individual is in terms of the effect on fitness including survival and fecundity. In this paper, we introduce a model of strategy evolution with network dynamics based on mortality selection. The intensity β of mortality selection has a nontrivial role in the evolution of both cooperation and network structure. At a defector's temptation b=1, the system gains its maximal cooperation level at β→∞. Increasing b decreases βmax for the maximal cooperation level. For network structure, the average degrees of strategists and the self-organization of clusters are investigated to understand the connections of strategists and their effects on cooperation level. Furthermore, we introduce the cooperating k-core to describe the tight level of the cooperator cluster. Cooperation is enhanced by forming a tight cooperating k-core at moderate β, but there is a collapse of the cooperating k-core when β is too large. The results indicate that cooperators outside the cooperating k-core play an important role in maintaining that core to ensure a high cooperation level. So the formation and maintenance of the cooperating k-core coordinate with each other at maximum cooperation level at a specific value of β.

Water dynamics in salt solutions studied with ultrafast two-dimensional infrared (2D IR) vibrational echo spectroscopy.

Water is ubiquitous in nature, but it exists as pure water infrequently. From the ocean to biology, water molecules interact with a wide variety of dissolved species. Many of these species are charged. In the ocean, water interacts with dissolved salts. In biological systems, water interacts with dissolved salts as well as charged amino acids, the zwitterionic head groups of membranes, and other biological groups that carry charges. Water plays a central role in a vast number of chemical processes because of its dynamic hydrogen-bond network. A water molecule can form up to four hydrogen bonds in an approximately tetrahedral arrangement. These hydrogen bonds are continually being broken, and new bonds are being formed on a picosecond time scale. The ability of the hydrogen-bond network of water to rapidly reconfigure enables water to accommodate and facilitate chemical processes. Therefore, the influence of charged species on water hydrogen-bond dynamics is important. Recent advances in ultrafast coherent infrared spectroscopy have greatly expanded our understanding of water dynamics. Two-dimensional infrared (2D IR) vibrational echo spectroscopy is providing new observables that yield direct information on the fast dynamics of molecules in their ground electronic state under thermal equilibrium conditions. The 2D IR vibrational echoes are akin to 2D nuclear magnetic resonance (NMR) but operate on time scales that are many orders of magnitude shorter. In a 2D IR vibrational echo experiment (see the Conspectus figure), three IR pulses are tuned to the vibrational frequency of interest, which in this case is the frequency of the hydroxyl stretching mode of water. The first two pulses "label" the initial molecular structures by their vibrational frequencies. The system evolves between pulses two and three, and the third pulse stimulates the emission of the vibrational echo pulse, which is the signal. The vibrational echo pulse is heterodyne, detected by combining it with another pulse, the local oscillator. Heterodyne detection provides phase and amplitude information, which are both necessary to perform the two Fourier transforms that take the data from the time domain to a two-dimensional frequency domain spectrum. The time dependence of a series of 2D IR vibrational echo spectra provides direct information on system dynamics. Here, we use two types of 2D IR vibrational echo experiments to examine the influence that charged species have on water hydrogen-bond dynamics. Solutions of NaBr and NaBF(4) are studied. The NaBr solutions are studied as a function of the concentration using vibrational echo measurements of spectral diffusion and polarization-selective IR pump-probe measurements of orientational relaxation. Both types of measurements show the slowing of hydrogen-bond network structural evolution with an increasing salt concentration. NaBF(4) is studied using vibrational echo chemical-exchange spectroscopy. In these experiments, it is possible to directly observe the chemical exchange of water molecules switching their hydrogen-bond partners between BF(4)(-) and other water molecules. The results demonstrate that water interacting with ions has slower hydrogen-bond dynamics than pure water, but the slowing is a factor of 3 or 4 rather than orders of magnitude.

EXTINCTIONS IN HETEROGENEOUS ENVIRONMENTS AND THE EVOLUTION OF MODULARITY

Extinctions of local subpopulations are common events in nature. Here, we ask whether such extinctions can affect the design of biological networks within organisms over evolutionary timescales. We study the impact of extinction events on modularity of biological systems, a common architectural principle found on multiple scales in biology. As a model system, we use networks that evolve toward goals specified as desired input–output relationships. We use an extinction–recolonization model, in which metapopulations occupy and migrate between different localities. Each locality displays a different environmental condition (goal), but shares the same set of subgoals with other localities. We find that in the absence of extinction events, the evolved computational networks are typically highly optimal for their localities with a nonmodular structure. In contrast, when local populations go extinct from time to time, we find that the evolved networks are modular in structure. Modular circuitry is selected because of its ability to adapt rapidly to the conditions of the free niche following an extinction event. This rapid adaptation is mainly achieved through genetic recombination of modules between immigrants from neighboring local populations. This study suggests, therefore, that extinctions in heterogeneous environments promote the evolution of modular biological network structure, allowing local populations to effectively recombine their modules to recolonize niches.

Evolution of network structure by temporal learning

We study the effect of learning dynamics on network topology. A network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity. This incorporates necessary competition between different edges. The final network we obtain is robust and has a broad degree distribution.

Modeling of Structure Evolution of Filled Elastomers under Uniaxial Elongation

The evolution of filler network structure in elastomers during elongation is investigated on the basis of computer modeling. The filler network is represented by spherical particles of different sizes, which are randomly filling a prismatic volume. No physical interaction forces between the particles on the molecular level are considered. The modeling is based on geometrical obstructions of particle ensembles. Interpenetrations of rigid particles during modeling of filling and subsequent stretching are forbidden, and only a minimal finite distance between the particles is allowed. The material is assumed to be incompressible. It was found that the number of nearest neighbors (coordination number) in a loosely filled material increases during stretching; however, in a densely filled material, this value decreases. The analysis of the size of unfilled spaces in the matrix has shown the occurrence of essential structural heterogeneities in the filler network. Stretching of a densely filled material results in appreciable mixing of particles whereby changes of the distances between originally neighboring particles exceed the macroscopic elongation of the material by several times. An analytical relation between the elongation of the composite and polymer chains via transmission ratio is proposed.

Network structure and thermal property of a novel high temperature seal glass

In this study, novel glasses based on SrO–La2O3–Al2O3–B2O3–SiO2 system are investigated for solid oxide fuel and electrolyzer cells. The network structure evolution of the glasses with increasing B2O3:SiO2 ratio was studied using Raman spectroscopy. The thermal properties of the glasses, including glass transition temperature T g and glass softening temperature T d , were studied using dilatometry. The thermal stability of the glasses was investigated using X-ray diffraction. The study shows that as the B2O3:SiO2 ratio increases, the SrO–La2O3–Al2O3–B2O3–SiO2 glass micro-heterogeneity and the amount of non-bridging oxygen atoms increase. Correspondingly, the T g of the SrO–La2O3–Al2O3–B2O3–SiO2 glasses changes from 635 to 775°C, and the T d changes from 670 to 815°C. Glass thermal stability decreases with B2O3:SiO2 ratio increase. The glass without B2O3 is thermally stable after being kept at 850°C for 200 hrs.

Network Structure and Design in the Deregulated U.S. Airline Industry: An Argument for Re-Regulation?

This paper develops a model to explain and analyze the evolution of network structure (connectivity) and design (flight frequency, aircraft size, prices) in the post-deregulation U.S. airline industry. We show that legacy carriers choice of Hub-and-Spoke networks and the emergence of low cost carriers (LCCs) operating Point-to-Point networks were optimal choices. We demonstrate that LCCs need not necessarily charge lower prices, and their entry impacted legacy carriers' prices in all markets, even those where there is no direct competition. We show that in response to entry, legacy carriers optimally lower flight frequency, leading to longer wait times between flights for which passengers are compensated by lower prices; conversely, if the entrant later exits, legacy carriers raise flight frequency and therefore prices, which may erroneously appear to be predatory pricing when in fact it is the consequence of optimal network redesign. Finally, we demonstrate that even though low cost carriers lower prices, total social welfare with competing network structures can also be lowered. In other words, the poor financial performance of legacy carriers is not due to their inefficiency per se but due to an efficient Hub-and-Spoke network undermined by competition from inefficient Point-to-Point networks. We argue that social welfare may have been, and still can be, higher if entry and exit in air passenger travel industry is regulated.

Topology control with IPD network creation games

Network creation games couple a two-players game with the evolution of network structure. A vertex player may increase its own payoff with a change of strategy or with a modification of its edge-defined neighbourhood. By referring to the iterated prisoners dilemma (IPD) game we show that this evolutionary dynamics converges to network-Nash equilibria, where no vertex is able to improve its payoff. The resulting network structure exhibits a strong dependence on the parameter of the payoff matrix. Degree distributions and cluster coefficients are also strongly affected by the specific interactions chosen for the neighbourhood exploration. This allows network creation games to be seen as a promising artificial-social-systems approach for a distributive topology control of complex networked systems.

Promotion of cooperation induced by the interplay between structure and game dynamics

We consider the coupled dynamics of the adaption of network structure and the evolution of strategies played by individuals occupying the network vertices. We propose a computational model in which each agent plays a n-round Prisoner's Dilemma game with its immediate neighbors, after that, based upon self-interest, partial individuals may punish their defective neighbors by dismissing the social tie to the one who defects the most times, meanwhile seek for a new partner at random from the neighbors of the punished agent. It is found that the promotion of cooperation is attributed to the entangled evolution of individual strategy and network structure. Moreover, we show that the emerging social networks exhibit high heterogeneity and disassortative mixing pattern. For a given average connectivity of the population and the number of rounds, there is a critical value for the fraction of individuals adapting their social interactions, above which cooperators wipe out defectors. Besides, the effects of the average degree, the number of rounds, and the intensity of selection are investigated by extensive numerical simulations. Our results to some extent reflect the underlying mechanism promoting cooperation.

A modified particle swarm optimization-based dynamic recurrent neural network for identifying and controlling nonlinear systems

In this paper, we first present a learning algorithm for dynamic recurrent Elman neural networks based on a modified particle swarm optimization. The proposed algorithm computes concurrently both the evolution of network structure, weights, initial inputs of the context units and self-feedback coefficient of the modified Elman network. Thereafter, we introduce and discuss a novel control method based on the proposed algorithm. More specifically, a dynamic identifier is constructed to perform speed identification and a controller is designed to perform speed control for Ultrasonic Motors (USM). Numerical experiments show that the novel identifier and controller based on the proposed algorithm can both achieve higher convergence precision and speed than other state-of-the-art algorithms. In particular, our experiments show that the identifier can approximate the USM’s nonlinear input–output mapping accurately. The effectiveness of the controller is verified using different kinds of speeds of constant, step and sinusoidal types. Besides, a preliminary examination on a randomly perturbation also shows the robust characteristics of the two proposed models.