Random Network Model Research Articles

A differential game approach for real-time security defense decision in scale-free networks

The current network security defense decision method is based on the principle of attack–defense confrontation and generally uses a random network model to project the real network. Most of the existing complex network defense decision methods consider a single defender while ignoring the confrontation and dependence between network attack and defense behaviors. In addition, the pertinence and dynamics of the defense effect are also insufficient. This article comprehensively considers the structure of a scale-free network and characteristics of network attack and defense behavior and combines a complex network with differential game theory to study the real-time defense decision problem. First, a propagation model based on a scale-free network is designed to realize the real-time fluctuation analysis of a network security state. Then, the attack and defense strategy are described, and the calculation method of revenue based on the confrontation analysis of the attack and defense behavior and a change in the security state of the network nodes based on the saddle point equilibrium strategy are introduced. Finally, based on the average connection degree of the real word Internet, a simulated network is constructed to conduct experiments. By comparison with random defense strategies and several typical complex network defense decision methods, the effectiveness and advancement of the proposed method are verified.

Routing Strategies for Isochronal-Evolution Random Matching Network.

In order to abstract away a network model from some real-world networks, such as navigation satellite networks and mobile call networks, we proposed an Isochronal-Evolution Random Matching Network (IERMN) model. An IERMN is a dynamic network that evolves isochronally and has a collection of edges that are pairwise disjoint at any point in time. We then investigated the traffic dynamics in IERMNs whose main research topic is packet transmission. When a vertex of an IERMN plans a path for a packet, it is permitted to delay the sending of the packet to make the path shorter. We designed a routing decision-making algorithm for vertices based on replanning. Since the IERMN has a specific topology, we developed two suitable routing strategies: the Least Delay Path with Minimum Hop (LDPMH) routing strategy and the Least Hop Path with Minimum Delay (LHPMD) routing strategy. An LDPMH is planned by a binary search tree and an LHPMD is planned by an ordered tree. The simulation results show that the LHPMD routing strategy outperformed the LDPMH routing strategy in terms of the critical packet generation rate, number of delivered packets, packet delivery ratio, and average posterior path lengths.

Unraveling the bond structure, porosity, and mechanical properties amorphous ZIF-4 and its topological equivalents: Large scale ab initio calculations

Four large-scale amorphous zeolite imidazolate frameworks (a-ZIFs) have been constructed based on the continuous random network model. Structural properties correlation (SPC) between the mechanical properties and structural order (short-range, mid-range, or long-range orders) of ZIFs samples have been investigated by well-defined density functional theory calculations. The results demonstrated the effect of short-range and mid-range local order on the mechanical properties of the a-ZIFs, respectively. By combining different metal nodes and organic ligands, the porosity of a-ZIFs can be dramatically changed, leading to changes in Young’s modulus and shear modulus. The most important, SPC between structural order and mechanical properties of a-ZIFs is established, which will pave the way to designing high-strength and toughness ZIF-based glass.

Volume relaxation of soda-lime silicate glasses below glass transition temperature

Structural relaxation is a widely known phenomenon that occurs in glassy systems, which still attracts strong industrial and research interest. Although the volume change associated with structural relaxation is well described by the Kohlrausch–Williams–Watts function, its origin, particularly from a glass structural viewpoint, is not clearly defined. To understand the behavior of structural relaxation, in this study, we performed volume relaxation evaluations, Raman spectroscopy assessments, and surface resistivity measurements before and after annealing at 50 K below the glass transition temperature Tg of soda-lime silicate glasses with the same Tg but different fragilities. The combined results indicated that the following changes in the glass structure occurred during the structural relaxation: (1) reorganization of the SiO2 network; (2) transfer of Na ions from the ion channel region into the SiO2 network region; and (3) segmentation of the ion channel region in the modified random network model.

Randomized Vector Network Model for Thyroid Prediction Using Relief And Lasso Feature Selection Approaches

The studies’ primary aim is to help the research scholars as a source who would like to research in the thyroid disease detection region. UC Irvin knowledge discovery provides databases files for the machine learning archives' thyroid dataset. Here, a random vector network model (RVNM) is proposed to perform classification tasks. The proposed model integrates the prior dataset information regarding the samples to train the more effective classifier. This cascaded random vector network model helps in thyroid disease prediction. The evaluation process is performed to predict and determine the respective performance concerning accuracy. The intuition is provided in this research, like forecasting the thyroid disease; it also calls attention to the process of using a Randomized Vector Network Model (RVNM) as a medium for classification. The simulation is done in the MATLAB 2020a environment and establishes a better trade-off than various existing approaches. The model gives a prediction accuracy of 96.1% accuracy compared to other models and shows a better trade than others.

Modulation of electromagnetic wave absorption via porosity in Pechini-derived carbon guided by a random network model.

It is well established that porosity in carbon materials can benefit electromagnetic wave absorption by providing stronger interfacial polarization, better impedance matching, multiple reflections, and lower density, but an in-depth assessment is still lacking on this issue. The random network model describes the dielectric behavior of a conduction-loss absorber-matrix mixture with two parameters related to the volume fraction and conductivity, respectively. In this work, the porosity in carbon materials was tuned by a simple, green, and low-cost Pechini method, and the mechanism of how porosity affects EM wave absorption was investigated quantitatively based on the model. It was discovered that porosity was crucial for the formation of a random network, and a higher specific pore volume led to a larger volume fraction parameter and a lower conductivity parameter. Guided by the high throughput parameter sweeping based on the model, the Pechini-derived porous carbon could achieve an effective absorption bandwidth of 6.2 GHz at 2.2 mm. This study further verifies the random network model, unveiling the implication and influencing factors of the parameters, and opens a new path to optimize the electromagnetic wave absorption performance of conduction-loss materials.

Percolation threshold and electrical conductivity of conductive polymer composites filled with curved fibers in two-dimensional space.

Quantifying the influence of fiber curvature on the percolation behavior of flexible conductive fiber and further on the electrical conductivity of conductive polymer composites (CPCs) is crucial for the design of CPCs. This study considers CPCs as a random packing of soft curved discorectangles (CDCRs) in a polymer matrix. The geometry of CDCR is developed, and an inter-CDCR contact detection algorithm is used to generate a random packing structure of CDCRs. The effects of aspect ratio α and bending central angles θ of CDCR on the percolation threshold ϕc of the overlapped CDCR system in a two-dimensional plane are then investigated using the finite-size scaling method. The result reveals that ϕc decreases monotonically as α grows and increases monotonically as θ rises. A shape-independent power law formula, denoted as ϕc = 2.2015 A-0.8172dex is developed to quantify the relationship between the Adex and ϕc. A comparison of our numerical simulations, published data, and predictions verifies the reliability and universality of the fitting model. Subsequently, a resistor network searching algorithm (RNSA) is proposed to construct the random resistor network model (RRNM). A power law model, denoted as is developed to evaluate the effects of the normalized reduced density (η - ηc)/ηc on the effective conductivity σeff of CPC. Comparing our predictions with data from the literature and our simulation verifies the reliability of our RNSA and the fitting model. This paper's methodology and findings may provide a theoretical hint for the CPC's design.

Retracted: A Random Matrix Network Model for the Network Teaching System of College Music Education Courses

Retracted: A Random Matrix Network Model for the Network Teaching System of College Music Education Courses

Evaluating the statistical similarity of neural network activity and connectivity via eigenvector angles

Neural systems are networks, and strategic comparisons between multiple networks are a prevalent task in many research scenarios. In this study, we construct a statistical test for the comparison of matrices representing pairwise aspects of neural networks, in particular, the correlation between spiking activity and connectivity. The ”eigenangle test” quantifies the similarity of two matrices by the angles between their ranked eigenvectors. We calibrate the behavior of the test for use with correlation matrices using stochastic models of correlated spiking activity and demonstrate how it compares to classical two-sample tests, such as the Kolmogorov–Smirnov distance, in the sense that it is able to evaluate also structural aspects of pairwise measures. Furthermore, the principle of the eigenangle test can be applied to compare the similarity of adjacency matrices of certain types of networks. Thus, the approach can be used to quantitatively explore the relationship between connectivity and activity with the same metric. By applying the eigenangle test to the comparison of connectivity matrices and correlation matrices of a random balanced network model before and after a specific synaptic rewiring intervention, we gauge the influence of connectivity features on the correlated activity. Potential applications of the eigenangle test include simulation experiments, model validation, and data analysis.

Analysis of Turkey’s airport network structure and centrality in the opening-out period after the first wave of COVID-19: A complex network approach

There are a few studies present analyzing air transport structures of the countries, regions, and the world by using complex network theory. Although Turkey has a complex air network with 56 airports, thorough research has not been carried out applying complex network analysis to reveal the structure of the airport network in Turkey yet. Furthermore, the fact that Turkey is a developing economy and the role of air transport in this development is undeniable, and its use as a transit area due to its geopolitical location, together with social and political factors, makes it important to understand this air transport network in detail. Consequently, in this paper, Turkey’s Airport Network (TAN) topology was explored by implementing concepts of complex network theory using domestic and international passenger flight data gathered from FlightRadar during the opening period after the first wave of Covid-19. The average path length and clustering coefficient for connectivity performance and centrality metrics (degree, betweenness, and closeness) were computed and the network correlations were also measured and compared with simulated random, small-world (SW), and scale-free (SF) network models. Also, structural similarities and differences with the air networks of other countries are revealed.

Study on Influencing Factors of Ethanol Coupling to Prepare C4 Olefins

In this paper, we study the data analysis and calculation of the factors influencing the preparation of C4 olefins by ethanol coupling. Specifically, by combining each catalyst, the relationship between temperature and ethanol conversion and C4 olefin selectivity was analyzed by regression analysis and correlation analysis, respectively. In addition, the grey relational degree analysis was used to solve the grey relational degree between the influencing factors of the experiment. The nonlinear correspondence between ethanol conversion and C4 olefin selectivity with different catalyst combinations and temperatures was evaluated by means of random forest model and BP neural network model. Finally, we build a support vector machine regression (SVMR) model and optimize the cost and gamma parameters of this model using a genetic algorithm.

Linear Regression and Its Inference on Noisy Network-Linked Data

AbstractLinear regression on network-linked observations has been an essential tool in modelling the relationship between response and covariates with additional network structures. Previous methods either lack inference tools or rely on restrictive assumptions on social effects and usually assume that networks are observed without errors. This paper proposes a regression model with non-parametric network effects. The model does not assume that the relational data or network structure is exactly observed and can be provably robust to network perturbations. Asymptotic inference framework is established under a general requirement of the network observational errors, and the robustness of this method is studied in the specific setting when the errors come from random network models. We discover a phase-transition phenomenon of the inference validity concerning the network density when no prior knowledge of the network model is available while also showing a significant improvement achieved by knowing the network model. Simulation studies are conducted to verify these theoretical results and demonstrate the advantage of the proposed method over existing work in terms of accuracy and computational efficiency under different data-generating models. The method is then applied to middle school students' network data to study the effectiveness of educational workshops in reducing school conflicts.

Probing the homogeneous distribution of sodium atoms in silicate glasses

Sodium, silicon, and oxygen—the major constituents of magmatic silicate melts in the Earth's crust and mantle—are also common ingredients in commercial flexible and scratch-proof glasses for advanced optoelectronic devices. The distribution of alkali metal cations in these amorphous networks is known to control their overall strength, flexibility, and chemical resistance. While the exact nature of their distribution remains a longstanding problem in understanding the glass structures, metal cations have often been expected to form heterogeneous nano-scale percolation channels where they have exclusive proximity towards nonbridging oxygens (NBOs; Na–O–Si species). Contrary to this picture, we show that nuclear magnetic resonance correlation spectroscopy between sodium and oxygen atoms demonstrates the presence of bridging oxygens (BOs; Si–O–Si species), as well as NBOs, in the immediate vicinity of sodium atoms. Our results indicate a metal distribution which is more homogeneous than the previously envisaged Modified Random Network (MRN) model, which represents the extreme end of heterogeneity with metal percolation channels encapsulated exclusively by nonbridging oxygens, by placing qualitative limits on the extent of segregation of alkali channels from silica-enriched regions as suggested by the MRN model. A refined structural model of oxide glass is proposed in terms of a “perturbed” metal ion distribution, which features a rather homogeneous metal dispersion with differences in Na affinity towards the bridging and non-bridging oxygen species. Such perturbed alkali distribution may be used to account for the anomalous viscosity of magmatic melts, as well as revealing the origins of pronounced toughness in complex oxide glasses.

A New Approach to Machine Learning Model Development for Prediction of Concrete Fatigue Life under Uniaxial Compression

The goal of this work is to show how machine learning models, such as the random forest, neural network, gradient boosting, and AdaBoost models, can be used to forecast the fatigue life (N) of plain concrete under uniaxial compression. Here, we developed our final machine learning model by generating the following three data files from the original data used in the work of Zhang et al.: (a) grouped data with the same input variable value and different output variable logN value, (b) data excluding outliers selected by three or more outlier detection methods; (c) average data excluding outliers, created by averaging the grouped data after excluding outliers from among the grouped data. Excluding the sustained strength of the concrete variable, originally treated as the seventh input variable in the work of Zhang et al., resulted in improving the determination coefficient (R2) values. Moreover, the gradient boosting model showed a high R2 value at 0.753, indicating a high accuracy in predicting outcomes. Further analysis using data excluding outliers shows that the R2 value increased to 0.803. Moreover, the average data excluding outliers provided the best R2 value at 0.915. Finally, a permutation feature importance (PFI) analysis was carried out to determine the strength of the relationship between the feature and the target value for the gradient boosting model. The analysis results showed that the maximum stress level (Smax) and loading frequency (f) were the most significant input variables, followed by compressive strength (f′c) and maximum to minimum stress ratio (R). Shape and height to width ratio (h/w) were the features with a non-significant influence on the model. This trend was previously confirmed by a Pearson and Spearman correlation analysis.

A Random Matrix Network Model for the Network Teaching System of College Music Education Courses

How to improve the teaching management model has always been an important part of the research and exploration of university music teaching management. Based on the random matrix network theory, this paper builds a random matrix network model and uses the random matrix network of Internet/Intranet to realize the electronic and networked multimedia information management, which makes the data query more flexible and convenient. In the random matrix network environment, the security of the model network is greatly improved, which solves the expansion problem of the random matrix network. During the simulation process, the model integrates the relevant image, audio, video, animation, and other multimedia processing technologies and storage technologies. The system adopts object-oriented analysis and design ideas to analyze the requirements, adopts the random matrix network architecture, and uses tomcat as the server, and the SQL Server database launched by Microsoft is used as the back-end data support, and the Struts architecture is designed by using the MVC development mode to ensure the system has good maintainability and enhanced data processing capabilities. The experimental results show that the response time of the system login verification is less than 2 seconds, the response time of adding users is less than 2 seconds, the response time of downloading assignments is less than 2 seconds, the response time of uploading courseware is less than 3 seconds, and the response time of checking the results is less than 2 seconds. The separation of technology effectively improves the scalability and maintainability of the system.

A Neural Network Model for the Relationship between Hotel Marketing Strategies and Performance Based on Nonlinear Random Matrix Theory

The homogenization of hotel competition has become a key problem faced by the hotel enterprises. According to the marketing theory, one perfect marketing strategy will improve hotel performance. Based on nonlinear random matrix and neural network model, this paper empirically analyzes the relationship between different marketing strategies and hotel performance from the aspects of hotel demand collection, strategy support, performance matching, and strategy regulation. The results show that the hotel performance based on product marketing strategies is the best, which has improving hotel performance about 5%. In contrast, the hotel performance based on people-oriented marketing strategies is the worst. Therefore, we believe that the hotel enterprises should pay attention to the adjustment of product structure and the role of people in hotel operation and management. Adjusting product structure, strengthening hotel information construction, and paying attention to talent cultivation play an important role in improving hotel performance, in order to provide useful reference for the development of other hotels.

An Efficient Outlier Detection with Deep Learning-Based Financial Crisis Prediction Model in Big Data Environment.

As Big Data, Internet of Things (IoT), cloud computing (CC), and other ideas and technologies are combined for social interactions. Big data technologies improve the treatment of financial data for businesses. At present, an effective tool can be used to forecast the financial failures and crises of small and medium-sized enterprises. Financial crisis prediction (FCP) plays a major role in the country's economic phenomenon. Accurate forecasting of the number and probability of failure is an indication of the development and strength of national economies. Normally, distinct approaches are planned for an effective FCP. Conversely, classifier efficiency and predictive accuracy and data legality could not be optimal for practical application. In this view, this study develops an oppositional ant lion optimizer-based feature selection with a machine learning-enabled classification (OALOFS-MLC) model for FCP in a big data environment. For big data management in the financial sector, the Hadoop MapReduce tool is used. In addition, the presented OALOFS-MLC model designs a new OALOFS algorithm to choose an optimal subset of features which helps to achieve improved classification results. In addition, the deep random vector functional links network (DRVFLN) model is used to perform the grading process. Experimental validation of the OALOFS-MLC approach was conducted using a baseline dataset and the results demonstrated the supremacy of the OALOFS-MLC algorithm over recent approaches.

Evaluation of Physical Education Teaching Quality Based on the Random Multivariate Matrix Convolution Neural Network Model

With the non-stop merchandising and improvement of bodily training instructing reform, the institution of a couple of getting to know the assessment and evaluation mannequin of bodily training different educating impact can stimulate the enthusiasm and activity in sports activities and learning, which is useful to domesticate students’ cognizance of lifelong bodily exercise. In the educating process, through the integration technique of varied instructing methods, this paper explores the issues present in the method of bodily training reform, places ahead a diverse bodily training instructing impact assessment mannequin that meets the wants of customized intelligence education and construction, and makes use of the technique of empirical evaluation to affirm the comparison model. In order to reflect the effect of physical training effectively and accurately, a convolution neural neighbourhood model based totally completely on the random multivariate matrix is proposed to reflect on consideration on the excellent of bodily education. The overall performance of the prediction accuracy assessment mannequin is evaluated via the simulation test and in contrast with the standard approach model. The experimental information is that the average assessment accuracy of the single convolution neural community mannequin is 82.15%, whilst the common comparison accuracy of the convolution neural community mannequin based totally on the random multivariate matrix is 97.58%, and the prediction accuracy is increased by means of 15.43%. The common prediction error of the single convolution neural community mannequin is 0.97, and the common error is 0.91, the common error is decreased by using 0.05. It shows that the random multivariate matrix convolution neural neighbourhood model can efficaciously realize the evaluation of instructing quality.

Fabric wrinkle rating model based on ResNet18 and optimized random vector functional-link network

To improve the accuracy and efficiency of the objective evaluation of the fabric wrinkle model, the R18-COTSA-RVFL model is proposed in this paper. The fabric wrinkle rating model is based on the combination of ResNet18 (R18) and the enhanced random vector functional-link network, in which the enhanced random vector functional-link network replaces the Softmax layer of ResNet18. This model uses ResNet18 (R18) to extract features from wrinkled images. In addition, a random vector functional-link network model improved by the tunicate swarm algorithm was proposed for wrinkle-level classification. First, we used chaotic-logistic maps and opposition-based learning to optimize the initial population of the tunicate swarm algorithm. The proposed search agent fragment mapping method enables the tunicate swarm algorithm simultaneously to optimize the hidden layer node parameters and bias, input layer weight, and activation function of the random vector functional-link network, thus avoiding the inaccurate classification result caused by random parameter selection in the random vector functional-link network. From comparative experiments, the proposed R18-COTSA-RVFL has the highest average classification accuracy and stability.

Research on the Current Situation of Employment Mobility and Retention Rate Predictions of “Double First-Class” University Graduates Based on the Random Forest and BP Neural Network Models

The economic development of various regions is influenced by high-quality population mobility. The research object of this article is the employment mobility data of “Double First-Class” university graduates from 2014 to 2019; the subsequent analysis is based on these data. First, this paper summarizes the current state of university graduates’ employment mobility. Second, this paper employs the fixed-effect model and PCA method to conclude that economic factors are the primary factors influencing university graduates’ employment mobility. Finally, based on the nonlinear, small sample, and high-dimensional characteristics of university graduates’ employment mobility data, this paper employs the random forest and BP neural network methods to build a prediction model for university graduates’ employment retention rate. The results show that the BP neural network model outperforms the random forest model in terms of prediction accuracy. The BP neural network model can accurately predict the employment retention rate of “Double First-Class” university graduates, which can guide the reasonable mobility of university graduates and provide a reference for government universities and individuals to make decisions.