Network Theory Methods Research Articles

Artificial Intelligence in Mathematical Modeling of Complex Systems

This article introduces artificial intelligence techniques in mathematical modelling of complex systems and their applications. Mathematical modelling of complex systems is a method of studying the structure and behaviour of complex systems, aiming to understand interactions and nonlinear effects in the system. Commonly used modelling methods include system dynamics, network theory, and algebraic methods. Artificial intelligence technologies include machine learning and deep learning, which can be used for tasks such as prediction and classification, anomaly detection, optimization and decision-making. In mathematical modelling of complex systems, artificial intelligence technology can learn system patterns and laws from large amounts of data, and can be applied to image and speech recognition, time series analysis and other fields. Deep learning and machine learning are important branches of artificial intelligence. They realize the modelling and analysis of complex systems by building neural network models. Data-driven modelling is a modelling method based on actual data that, combined with traditional theoretical modelling, can better describe and predict the behaviour of complex systems. Self-control of complex systems means that the system realizes its own optimization and adjustment through adaptive control algorithms and feedback mechanisms. In summary, artificial intelligence technology has broad application prospects in mathematical modelling of complex systems and will provide new tools and methods for in-depth understanding and solving problems in complex systems.

Logistic probability function: an analysis and modification from the perspective of reaction network theory

In this work we study some characteristics of growth functions of the logistic type. The standard logistic function and the 2-logistic growth function are solutions of ordinary differential equations derived from the perspective of reaction network theory. These solutions are compared in terms of their shape. We are interested in the new 2-logistic probability distribution and its characteristics. Using the tools of reaction network theory and numerical methods we derive some properties of this distribution.

How Does the Time-Varying Network Structure Evolve between the EU Carbon Futures Prices and Industrial and Energy-Related Indices? A Study Based on a Time-Varying T-Copula

Industrial and energy-related industries are major sources of carbon dioxide emissions, and their interdependence, as reflected in the financial field, has attracted the attention of scholars. For the purpose of exploring the evolutionary characteristics of the short-term dynamic correlation coefficient between the EU carbon futures price and the industrial and energy-related indices, this paper selected the settlement price of EU carbon emission quota futures, the MSCI energy I index on three dimensions, and the Dow Jones industrial index and West Texas crude oil futures price, as sample data. Using the time-varying t-copula model to measure the dynamic correlation coefficient between variables, the time-sliding window idea and coarse-grained method were combined to establish the correlation fluctuation mode, and a complex network theory and analysis methods were used to study the evolutionary traits of the time-varying network structure between the EU carbon price and the industrial and energy-related index. The results show that the transmission objects of the key correlation fluctuation modes in the network are stable and maintain their own state with a high probability. Second, the clustering effect exists in the transmission process. Some nodes with high mediating abilities are also the key correlation wave modes in the dynamic correlation evolution network. This study provides ideas for the study of the correlations between multiple variables and is also a useful reference for international investors.

Actor Relations in the Change of Shape and Space in the Rumah Gadang of Balai Kaliki Traditional Village

Rumah Gadang is a traditional building that represents the cultural identity of Minangkabau Tribe in West Sumatera. This traditional building is a cultural heritage that must be preserved. However, preservation led to many changes in the form and space of traditional buildings. Rumah Gadang which is located in Kampung Adat Balai Kaliki, Payakumbuh City West Sumatera has a level of change in minor and major. That matter could happen because of differences in the network of actor relations in each building. The factor of actor relations has an impact to change the preservation of traditional buildings, thus providing a new perspective on the preservation of traditional buildings. Therefore, this research aims to observe the changes that occur in the Rumah Gadang located in Kampung Adat Balai Kaliki through actor network’s perspective. Completion of this research using a qualitative methodology with Actor Network Theory (ANT) method by examining changes that occur in Rumah Gadang holistically, which is through a study the relations between the actors involved in that building to discorver the factors that cause the changes. This research find that the relation of actors involved in each Rumah Gadang determines the quantity of building changes. The result is charted using sosiotechnogram to know the strong actors in actor relations from each Rumah Gadang. Therefore, this research could be the basis for appropriate practices of preserving Rumah Gadang traditional building.

Functional characteristics of hub and wave-initiator cells in β cell networks

Islets of Langerhans operate as multicellular networks in which several hundred β cells work in synchrony to produce secretory pulses of insulin, a hormone crucial for controlling metabolic homeostasis. Their collective rhythmic activity is facilitated by gap junctional coupling and affected by their functional heterogeneity, but the details of this robust and coordinated behavior are still not fully understood. Recent advances in multicellular imaging and optogenetic and photopharmacological strategies, as well as in network science, have led to the discovery of specialized β cell subpopulations that were suggested to critically determine the collective dynamics in the islets. In particular hubs, i.e., β cells with many functional connections, are believed to significantly enhance communication capacities of the intercellular network and facilitate an efficient spreading of intercellular Ca2+ waves, whereas wave-initiator cells trigger intercellular signals in their cohorts. Here, we determined Ca2+ signaling characteristics of these two β cell subpopulations and the relationship between them by means of functional multicellular Ca2+ imaging in mouse pancreatic tissue slices in combination with methods of complex network theory. We constructed network layers based on individual Ca2+ waves to identify wave initiators, and functional correlation-based networks to detect hubs. We found that both cell types exhibit a higher-than-average active time under both physiological and supraphysiological glucose concentrations, but also that they differ significantly in many other functional characteristics. Specifically, Ca2+ oscillations in hubs are more regular, and their role appears to be much more stable over time than for initiator cells. Moreover, in contrast to wave initiators, hubs transmit intercellular signals faster than other cells, which implies a stronger intercellular coupling. Our research indicates that hubs and wave-initiator cell subpopulations are both natural features of healthy pancreatic islets, but their functional roles in principle do not overlap and should thus not be considered equal.

EVALUATING AGE-FRIENDLY COMMUNITY INITIATIVES FROM A SOCIAL NETWORK PERSPECTIVE

Abstract Age-friendly community initiatives (AFCIs) as community interventions systematically cultivate inter-organizational partnerships toward making policies, systems, and environments more supportive of healthy aging, aging in community, and aging equity. Building from a multi-year, university-community partnership to spur the development of AFCIs in New Jersey, we drew on social network theory and research methods to examine the networks of AFCI core teams and their key partners across diverse community settings. Data collection involved administering questionnaires to both AFC core teams and their key organizational partners. We report on the presence and strengths of dyadic relationships that comprise the networks, analyzed by sector, organizational function, and geographic service area of each network node. We discuss implications of the findings concerning policies and practices around sustainability planning for AFCIs as well as future directions for using network theory to understand AFC progress at various levels of geographic scope.

A Deep Contextual Bandit-Based End-to-End Slice Provisioning Approach for Efficient Allocation of 5G Network Resources

5G networks have been experiencing challenges in handling the heterogeneity and influx of user requests brought upon by the constant emergence of various services. As such, network slicing is considered one of the critical technologies for improving the performance of 5G networks. This technology has shown great potential for enhancing network scalability and dynamic service provisioning through the effective allocation of network resources. This paper presents a Deep Reinforcement Learning-based network slicing scheme to improve resource allocation in 5G networks. First, a Contextual Bandit model for the network slicing process is created, and then a Deep Reinforcement Learning-based network slicing agent (NSA) is developed. The agent’s goal is to maximize every action’s reward by considering the current network state and resource utilization. Additionally, we utilize network theory concepts and methods for node selection, ranking, and mapping. Extensive simulation has been performed to show that the proposed scheme can achieve higher action rewards, resource efficiency, and network throughput compared to other algorithms.

Application of Long-Term and Short-Term Memory Neural Network in Technical Evaluation of Hurdle Track and Field

In order to study the application of long-term and short-term memory neural networks in the technical evaluation of hurdle track and field, firstly, the related contents of long-term and short-term memory neural networks and hurdle track and field sports are analyzed, the variation characteristics of kinematics and surface electromyography of swinging leg in the landing link of lower hurdle are analyzed, and the relationship between surface electromyography and kinematics parameters is analyzed. Then, the kinematics and surface EMG data of three male hurdlers in the hurdle team and two men in the College of Physical Education were collected when completing the technical link of pressing down and landing. The subject data were processed and analyzed by using the Simi motion video analysis system and DASY lab10.0 EMG analysis software. Finally, SPSS13.0 and origin are used to analyze the average, standard deviation, and correlation of the data results and draw a graph. Experiments show that neural network theory and finite element method, as the new forces of sports biomechanics, have been used to diagnose track and field technology. With the popularization of this method, it will play a greater role in the research of track and field technology.

System vulnerability to flood events and risk assessment of railway systems based on national and river basin scales in China

Abstract. Floods have negative effects on the reliable operation of transportation systems. In China alone, floods cause an average of ∼1125 h of railway service disruptions per year. In this study, we present a simulation framework to analyse the system vulnerability and risk of the railway system to floods. First, we developed a novel methodology for generating flood events at both the national and river basin scale. Based on flood hazard maps of different return periods, independent flood events are generated using the Monte Carlo sampling method. Combined with network theory and spatial analysis methods, the resulting event set provides the basis for national- and provincial-level railway risk assessments, focusing in particular on train performance loss. Applying this framework to the Chinese railway system, we show that the system vulnerability of the Chinese railway system to floods is highly heterogeneous as a result of spatial variations in the railway topology and traffic flows. Flood events in the Yangtze River basin show the largest impact on the national railway system, with approximately 40 % of the national daily trains being affected by a 100-year flood event in that basin. At the national level, the average percentage of daily affected trains and passengers for the national system is approximately 2.7 % of the total daily number of trips and passengers. The event-based approach presented in this study shows how we can identify critical hotspots within a complex network, taking the first steps in developing climate-resilient infrastructure.

Research on innovation features and optimization strategies of industrial clusters from the perspective of TLCN

PurposeThe focus of industrial cluster innovation lies in the cooperation between enterprises and universities/scientific research institutes to make a theoretical breakthrough in the system and mechanism of industrial cluster network. Under the theoretical framework of cluster network, industrial structure can be optimized and upgraded, and enterprise benefit can be improved. Facing the increasing proliferation and multi-structured enterprise data, how to obtain potential and high-quality innovation features will determine the ability of industrial cluster network innovation, as well as the paper aims to discuss these issues.Design/methodology/approachBased on complex network theory and machine learning method, this paper constructs the structure of “three-layer coupling network” (TLCN), predicts the innovation features of industrial clusters and focuses on the theoretical basis of industrial cluster network innovation model. This paper comprehensively uses intelligent information processing technologies such as network parameters and neural network to predict and analyze the industrial cluster data.FindingsFrom the analysis of the experimental results, the authors obtain five innovative features (policy strength, cooperation, research and development investment, centrality and geographical position) that help to improve the ability of industrial clusters, and give corresponding optimization strategy suggestions according to the result analysis.Originality/valueBuilding a TLCN structure of industrial clusters. Exploring the innovation features of industrial clusters. Establishing the analysis paradigm of machine learning method to predict the innovation features of industrial clusters.

Essays in Labor Market Employing Network Theory and Decomposition Methods

Essays in Labor Market Employing Network Theory and Decomposition Methods

Analysis on the Transmission of International Oil Price to China’s Price Index--Based on Granger Causality Network

This paper uses complex network theory and Granger causality method to construct Granger Causality Network among price indices (PIGCN), specifically based on crude oil price and China’s domestic price index from a systematic perspective and provides a statistical physical method to analyze conduction fluctuations. This paper selects four typical price indices in China, including 36 sub price indices as samples for empirical research. Through the analysis of the PIGCN structure, we have analyzed the volatility transmission path of the international crude oil price and Chinese domestic price index, and further revealed the dynamic relationship among the price indices. Also, we have put forward some policy recommendations to better monitor the spread of price fluctuation risks.

Dynamical behavior and optimal control of a vector-borne diseases model on bipartite networks

For better application of vaccination control and quarantine control, a novel vector-borne diseases model with two control strategies on bipartite networks is proposed. By using complex network theory and Lyapunov function method, the basic reproduction number is obtained, and the global stability of disease-free equilibrium and endemic equilibrium are proved. Then the time-varying optimal control problem for reducing control cost is investigated. Using the optimal control theory, the existence and uniqueness of the optimal control and the model’s optimal solution are both studied. Finally, numerical simulations are carried out to verify the analysis results and the efficiency of the optimal control.

On the Control of Psychological Networks.

The combination of network theory and network psychometric methods has opened up a variety of new ways to conceptualize and study psychological disorders. The idea of psychological disorders as dynamic systems has sparked interest in developing interventions based on results of network analytic tools. However, simply estimating a network model is not sufficient for determining which symptoms might be most effective to intervene upon, nor is it sufficient for determining the potential efficacy of any given intervention. In this paper, we attempt to remedy this gap by introducing fundamental concepts of control theory to both psychometricians and applied psychologists. We introduce two controllability statistics to the psychometric literature, average and modal controllability, to facilitate selecting the best set of intervention targets. Following this introduction, we show how intervention scientists can probe the effects of both theoretical and empirical interventions on networks derived from real data and demonstrate how simulations can account for intervention cost and the desire to reduce specific symptoms. Every step is based on rich clinical EMA data from a sample of subjects undergoing treatment for complicated grief, with a focus on the outcome suicidal ideation. All methods are implemented in an open-source R package netcontrol, and complete code for replicating the analyses in this manuscript are available online.

High-frequency trading and networked markets

Financial markets have undergone a deep reorganization during the last 20 y. A mixture of technological innovation and regulatory constraints has promoted the diffusion of market fragmentation and high-frequency trading. The new stock market has changed the traditional ecology of market participants and market professionals, and financial markets have evolved into complex sociotechnical institutions characterized by a great heterogeneity in the time scales of market members' interactions that cover more than eight orders of magnitude. We analyze three different datasets for two highly studied market venues recorded in 2004 to 2006, 2010 to 2011, and 2018. Using methods of complex network theory, we show that transactions between specific couples of market members are systematically and persistently overexpressed or underexpressed. Contemporary stock markets are therefore networked markets where liquidity provision of market members has statistically detectable preferences or avoidances with respect to some market members over time with a degree of persistence that can cover several months. We show a sizable increase in both the number and persistence of networked relationships between market members in most recent years and how technological and regulatory innovations affect the networked nature of the markets. Our study also shows that the portfolio of strategic trading decisions of high-frequency traders has evolved over the years, adding to the liquidity provision other market activities that consume market liquidity.

Social network research hotspots and trends in public health: A bibliometric and visual analysis

ObjectivesTo understand the research landscape and identify the research hotspots and trends of the application of social network theory and analysis to public health. Study designA bibliometric study of publications regarding application of social network theory and analysis to public health. MethodsChoosing 1607 articles about the application of social network theory and analysis to public health from the core collection database of Web of Science published from 1991 to 2020 as the research sample. A bibliometric and visual analysis of publication quantity and content was performed to analyze time trends, spatial distribution, cooperation networks, influential references, and keyword co-occurrence, clusters, and emergence. ResultsThere is an increasing trend in the use of social network theory and analysis in the public health field, with the United States taking the lead. Research focuses include on transmission of diseases or behavior through social networks and the influence of social networks on population health at different ages. Current research frontiers primarily include the role of social networks in tracking of emerging infectious diseases like COVID-19, preventing and controlling chronic diseases, and carrying out healthy behavioral interventions. ConclusionsThis study provides a comprehensive quantitative overview of the historic development of and latest topics in the application of social network theory and analysis method to public health. More attention should be paid to the important role of social networks in tracing the emergence of serious infectious diseases like COVID-19, as well as preventing and controlling chronic diseases and intervening in health behaviors, considering the increasing challenges and opportunities presented by online social networking.

The ecological network approach to algal-bacterial relationships: Review and prospects

<p indent=0mm>Algae and bacteria numerically dominate oceanic and freshwater planktonic communities. They play a central biogeochemical role in the marine ecosystem, including producing biomass to support aquatic food webs, cycling nutrients and trace elements, and decomposing organic matter. Algae and bacteria synergistically affect each other’s physiology and metabolism and have coexisted ever since the early stages of evolution that revolutionized many aspects of life on Earth. The relationship between algae and bacteria influences ecosystems and represents all conceivable modes of interaction from mutualism to parasitism. The interactions between these two groups of plankton, and the influence of their interaction on each other and on a global scale have, therefore, become the focus of recent research interest. Previous studies have summarized a variety of algal-bacterial interactions and aimed to reveal the essence of those interactions. However, it is clear that their interactions are diverse and not static, and can be initiated and broken in response to environmental and developmental cues. Traditional research methods have been unable to reveal the complexity behind algal-bacterial symbiosis. As microbial omics technology has developed and the influence of global change on marine ecosystems has become apparent, the research perspective on algal-bacterial relationships has gradually risen to the level of systemic ecology. The rapid-blooming of high-throughput sequencing technology and the ability to analyze big data offer new opportunities for a thorough study of algal-bacterial interaction. Ecological networks are prominent among these new methods: They can integrate multiple types of information and might represent systems-level behavior. Network theory methods are powerful approaches for quantifying relationships between biological components and their relevance to phenotypes, environmental conditions or other external variables within a system. In this paper, we review the research advances in the algal-bacterial ecological network based on big data. We begin by introducing the network parameters and attributes commonly used in ecological network analysis and review the existing network modeling methods in microbial studies and algorithm features and application scenarios. By comparing some common algorithms or software, researchers can choose appropriate network construction methods when confronted with different data characteristics. Next, the ecological network analysis used in the study of algal-bacterial relationships is presented at multiple layers from taxonomy to function (e.g., molecular and metabolic networks). The problems and experiences encountered during research into algal-bacterial relationships are then discussed. For example, when focusing on the interaction between host and microorganism, the time/space shift correlations among the variables will lead to wrong conclusions, including false-positive or false-negative results. We also attempt to introduce metabolic and dynamic biological networks into the study of the algal-bacterial relationship to reveal further the mechanism of their interaction from the perspectives of function and causality. Finally, our novel proposal to establish a knowledge network database of algal-bacterial interactions and a graphic database of photosphere biological profiles is discussed. We highlight the need for microbial ecological network inference and suggest strategies to infer networks more reliably. Our study aimed to explore the holistic information behind algal-bacterial relationships and provide new cues for further revealing the molecular mechanisms of algal-bacterial interactions.

Dry Port-Seaport Logistics Network Construction under the Belt and Road Initiative: A Case of Shandong Province in China

In recent years, China’s dry ports have entered into a period of rapid development, especially driven by the Belt and Road initiative (BRI). This initiative not only provides valuable opportunities, but also intensifies fierce competition between ports. In order to earn more profits and maintain an advantageous position, dry ports generally tend to rely on seaports to increase competitiveness and attract more goods, and seaports are also willing to cooperate with dry ports to expand their business. As a large coastal province in China, Shandong Province has good maritime resources. Because of the fierce competition among seaports inside and outside Shandong Province, port operators should make good use of the opportunity offered by BRI to make connections with the inland cities through the dry port-seaport logistics network and enhance the competitiveness. In order to take an active part in the process of the BRI, forming a dry port-seaport logistics network is a win-win strategy for Shandong province. The research first analyzes the impact of the Belt and Road initiative on ports of Shandong and its development, then uses complex network theory and TOPSIS method to select dry port candidate cities from BRI’s important transportation nodes. After considering economic benefits, carbon emissions and construction costs, a multi-objective optimization model is established with a construction cost preference coefficient. Then the NSGA-II algorithm is used to solve the realistic problem. The study finds that when the construction cost increases, the transportation cost and carbon emission cost will decrease, which indicates that the dry port-seaport logistics network established under the BRI can reduce the cost of logistics transportation and environmental pollution.

Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly-available nodes approach.

IntroductionUsing brain network and graph theory methods to analyze the Alzheimer's disease (AD) and mild cognitive impairment (MCI) abnormal brain function is more and more popular. Plenty of potential methods have been proposed, but the representative signal of each brain region in these methods remains poor performance.MethodsWe propose a highly‐available nodes approach for constructing brain network of patients with MCI and AD. With resting‐state functional magnetic resonance imaging (rs‐fMRI) data of 84 AD subjects, 81 MCI subjects, and 82 normal control (NC) subjects from the Alzheimer's Disease Neuroimaging Initiative Database, we construct connected weighted brain networks based on the different sparsity and minimum spanning tree. Support Vector Machine of Radial Basis Function kernel was selected as classifier.ResultsAccuracies of 74.09% and 77.58% in classification of MCI and AD from NC, respectively. We also performed a hub node analysis and found 18 significant brain regions were identified as hub nodes.ConclusionsThe findings of this study provide insights for helping understanding the progress of the AD. The proposed method highlights the effectively representative time series of brain regions of rs‐fMRI data for construction and topology analysis brain network.

Reimaging stakeholder analysis in project management: network theory and fuzzy logic applications

PurposeThe methods presently used for project stakeholder analysis have typically followed two distinct patterns: (1) a project-centric approach that places a project at the center, and subsequently, concentrates on dyadic relationships between the project and its stakeholders; (2) a network theory-based approach that emphasizes on the interconnections within the network of project stakeholders. The main contention of this study is built upon the premise that neither the sole analysis of dyadic relationships between a project and its stakeholders nor the stand-alone use of the network theory methods is adequate for reliable analysis of stakeholders.Design/methodology/approachThis article proposes a model that bases the salience of stakeholders on their relationships with the project as well as on their interdependencies in the project. In doing so, this work explores the potential of a Fuzzy Inference System (FIS) to provide a comprehensive picture of stakeholder analysis. Using a real-world biodiversity project, this paper analyses the project stakeholders based on their possession of various attributes as well as the extent to which each individual stakeholder influences the entire connected network of all stakeholders.FindingsA salient feature of the proposed FIS model is its ability to provide a high capacity for analyzing the results. The model is able to generate the input–output relationship surface view for stakeholder analysis. Further, unlike the traditional project stakeholder analysis methods that are linear, the proposed model is strongly nonlinear. This implies that change in the input variables of the fuzzy-based model is not expected to lead to a proportional change in the model output.Practical implicationsTwo practical implications can be drawn from the presented stakeholder analysis model. First, confronted with mounting pressure to understand the stakeholder environment and to effectively manage stakeholders, project managers need to establish a sound stakeholder management strategy. The stakeholder analysis model developed herein casts a wider net for the critical ranking of stakeholders in a project, thereby providing a more accurate prioritization of the stakeholders. Second, while stakeholders independently require managerial attention, understanding the effect of competing and cooperative stakeholder interactions are unarguably of great importance. The presented model prompts the project managers to recognize not only the influence of key stakeholders on the project but also the interactions of multiple stakeholders within the stakeholder network.Originality/valueThe proposed stakeholder analysis model possesses several desirable features. First, it is not constrained to capturing only stakeholder attributes discussed in the example project provided in this study. The model is flexible and adaptable to all business and management contexts. Second, the stakeholder mapping in the model is not a function of a sole attribute but rather a cumulative effect of multiple stakeholder attributes. In fact, the power of the suggested model lies in its ability to incorporate the three aspects of stakeholder theory into a single model. Third, the presented model builds a quantitative and qualitative picture of the stakeholder salience. The suggested FIS model is capable of processing both qualitative perception of stakeholder attributes and quantitative analysis of the network of stakeholder interactions. This allows for a more comprehensive and synergistic utilization of model inputs.