Network Theory Research Articles

INNOVATIVE MATHEMATICAL MODELLING IN PREDATOR-PREY DYNAMICS: A SYSTEMATIC REVIEW

Advancements in mathematical ecology have greatly expanded predator-prey models beyond the classical Lotka-Volterra framework, incorporating complex factors such as role reversal, fractional calculus, and the Allee effect. This review highlights innovative models and advanced mathematical techniques that enhance our understanding of predator-prey dynamics and their applications across disciplines. It analysed recent studies introducing new ecological factors, including maturation delays, disease dynamics, and spatial heterogeneity. Techniques like fractional calculus and network theory were examined for their effectiveness in capturing complex behaviours. Models addressing role reversal between adult prey and juvenile predators or incorporating generalized fractional derivatives reveal significant impacts on population stability. Additionally, maturation delays, handling time, and gestation periods markedly influence oscillatory dynamics. The reviewed models demonstrate versatility in guiding pest control, understanding disease spread, and optimizing biotechnological processes. This review shows that modern predator-prey models, enriched by complex ecological factors and advanced mathematics, provide profound insights into system dynamics, with practical applications across various fields. As global challenges grow, these models offer crucial guidance for developing more resilient and sustainable systems, underscoring their potential to addressissues like food security, disease outbreaks, and ecosystem degradation. Future research should integrate emerging factors, such as anthropogenic noise and industrial pollutants, to further enhance the models’ real-world relevance.

What Next for Actor Network Theory? Inventing around Latour on a planet in distress

This is a reflection piece on the theme ‘what will happen to actor-network theory (ANT) after Latour?’ We distinguish three scenarios but focus on the one we see as the least probable but most interesting for sociology and related disciplines. With inspiration from the anthropologist Marilyn Strathern, we think of this third scenario as a way of “inventing around” Latour. In this scenario, classical ANT tenets are prolonged and refracted through Latour's later work on modes of existence and critical zones. This mutant ANT is subject to further transformations through encounters with empirical situations and concepts from many other places. We use two brief cases for illustration. The first considers the empirical potentials and possible pitfalls of Latour's modes of existence in relation to the emergence of carbon markets as a form of Anthropocene politics. The second examines recent urban upheavals and transformations in Bangkok through the lens of urban critical zones, which make visible problems of co-existence that shade into pluriversal cosmopolitics because the city is full of beings that are both modern and amodern. These cases point the way to a scenario in which ANT is still unfinished, and far from “uncritical”. It is an “indisciplinary” scenario, where ANT with sociology and adjacent disciplines venture into complex landscapes of planetary ecological disruptions and postcolonial tensions without any false protection from certified theory and pre-defined politics.

Modeling and Analysis of Public Transport Network in Hohhot Based on Complex Network

In the urban public transport network, the transfer of buses and subways provides convenience for residents to travel efficiently. But in actual operation, it is found that accidents, natural disasters, and other damage are inevitable. These sudden events may lead to route suspensions and service delays, ultimately resulting in network paralysis. In this paper, complex network theory is used to construct a weighted double-layer network model. Carrying capacity is considered the edge weight. The model analyzes the impact of these sudden events on network performance. It also conducts in-depth research on network structure and node importance. A collective influence (CI) algorithm is proposed as a centrality index to evaluate node importance. Based on the dynamic nature of the attacks, the network state is divided into initial network and current network. Taking Hohhot as an example, the results show that the network based on a CI algorithm node attack has the worst invulnerability. The network invulnerability based on an edge weight attack is better than that of edge betweenness. Compared with the current network, the invulnerability of the initial network is stronger. This indicates that ongoing changes and adaptations in the network may accelerate the decline in overall performance. At the same time, targeted interventions on key nodes and edges can enhance the network’s invulnerability. Planners can continuously monitor network performance to provide a basis for dynamic management and real-time adjustments. Additionally, effective information about critical routes to the public helps ensure the sustainable operation of the public transportation network.

Robust Optimization Models for Planning Drone Swarm Missions

This article presents methods of planning unmanned aerial vehicle (UAV) missions in which individual platforms work together during the reconnaissance of objects located within a terrain. The planning problem concerns determining the flight routes of a swarm, where each UAV has the ability to recognize an object using a specific type of sensor. The experiments described in this article were carried out for drone formation; one drone works as a swarm information hub and exchanges information with the ground control station (GCS). Numerical models for mission planning are presented, which take into account the important constraints, simplifying the description of the mission without too much risk of losing the platforms. Several types of objective functions were used to optimize swarm flight paths. The mission models are presented in the form of mixed integer linear programming problems (MILPs). The experiments were carried out on a terrain model built on the basis of graph and network theory. The method of building a network on which the route plan of a drone swarm is determined is precisely presented. Particular attention was paid to the description of ways to minimize the size of the network on which the swarm mission is planned. The presented methods for building a terrain model allow for solving the optimization problem using integer programming tasks.

RBFNN-PSO Intelligent Synchronisation Method for Sprott B Chaotic Systems with External Noise and Its Application in an Image Encryption System.

In the past two decades, research in the field of chaotic synchronization has attracted extensive attention from scholars, and at the same time, more synchronization methods, such as chaotic master-slave synchronization, projection synchronization, sliding film synchronization, fractional-order synchronization and so on, have been proposed and applied to chaotic secure communication. In this paper, based on radial basis function neural network theory and the particle swarm optimisation algorithm, the RBFNN-PSO synchronisation method is proposed for the Sprott B chaotic system with external noise. The RBFNN controller is constructed, and its parameters are used as the particle swarm particle optimisation parameters, and the optimal values of the controller parameters are obtained by the PSO training method, which overcomes the influence of external noise and achieves the synchronisation of the master-slave system. Then, it is shown by numerical simulation and analysis that the scheme has a good performance against external noise. Because the Sprott B system has multiple chaotic attractors with richer dynamics, the synchronization system based on Sprott B chaos is applied to the image encryption system. In particular, the Zigzag disambiguation method for top corner rotation and RGB channel selection is proposed, and the master-slave chaotic system synchronisation sequences are diffused to the disambiguated data streams, respectively. Therefore, the encryption and decryption of image transmission are implemented and the numerical simulation results are given, the random distribution characteristics of encrypted images are analysed using histogram and Shannon entropy methods, and the final results achieve the expected results.

Institutional investor networks and earnings management: The role of the exit threat

This study examines the potential of institutional investor networks in mitigating horizontal agency conflicts, addressing the gap in the regulatory role of institutional investors in concentrated equity markets. Using data on Chinese listed companies from 2007 to 2020, we employ the multiplex network method to investigate the impact of institutional investor networks on earnings management. Our findings reveal a significant negative correlation between institutional investor networks and earnings management. The mechanism analysis indicates that institutional investor networks primarily exert governance through the exit threat rather than through direct intervention and information dissemination. Collectively, our findings elucidate the important role of institutional investors in corporate governance and promote the application of multiplex network theory to the financial field.

Study on the sports biomechanics prediction, sport biofluids and assessment of college students’ mental health status transport based on artificial neural network and expert system

Based on the theories of artificial neural networks and expert systems, this study constructs a model for assessing the sports biomechanics’ mental health of college students using artificial neural networks and expert systems. It assesses the sports biomechanics’ mental health status of college students who had their yoga classes suspended during the COVID-19 pandemic. Meanwhile, A mobile questionnaire was used to collect information on students’ personal circumstances, sport biofluids and yoga exercise routines, as well as data from the Depression Anxiety Stress Scales (DASS-21). This study presents a novel approach to assessing the intersection of sports biomechanics and mental health by employing Artificial Neural Networks (ANNs) and expert systems. Unlike previous research in this domain, this study offers an extensive review of the literature, highlighting both the distinctive contributions of ANNs and expert systems and the existing gaps in current methodologies. Similarly, a univariate analysis method was utilized to quantitatively assess the impact of yoga interventions and other factors on college students’ sports biomechanics and mental health. Building on this analysis, an artificial neural network (ANN) model was developed to predict mental health outcomes and sport biofluid conditions. The model focused on evaluating the significance of various variables, with particular attention to the contribution of yoga exercise routines. In short, this approach aims to enhance the understanding and support for utilizing yoga interventions to improve college students’ mental health within the context of sports biomechanics, especially in the post-pandemic era. The findings should make an important contribution to the field of integrating ANNs with expert systems and sports biomechanics improves mental health prediction accuracy.

BUGTGNN: Bayesian Uncertainty and Game Theory for Enhancing Graph Neural Networks through Mathematical Analysis

The ongoing COVID-19 pandemic has emphasized the critical need for rapid and accurate diagnostic methods. Precise classification of chest X-ray images into COVID-19 and non-COVID-19 cases serves as a pivotal tool in effective disease management and control. Existing methods often suffer from trade-offs between accuracy, precision, and computational efficiency, hindering their practical utility. Current approaches mainly rely on traditional machine learning algorithms or Convolutional Neural Networks (CNNs), which while effective, still present limitations in terms of sensitivity, specificity, and computational speed. These constraints necessitate the exploration of innovative techniques for improving classification metrics across multiple dimensions. In this work, we introduce a novel framework for optimizing Graph Neural Networks (GNNs) through mathematical analysis, specifically incorporating spectral methods, dynamic graph sparsification, game-theoretic attention mechanisms, Bayesian uncertainty models, and advanced graph partitioning techniques. When applied to the classification of COVID-19 chest X-rays, our model demonstrated significant improvements—increasing precision by 8.3%, accuracy by 8.5%, recall by 4.9%, specificity by 4.5%, and the Area Under the Curve (AUC) by 5.9%, while simultaneously reducing computational delay by 10.5% across multiple datasets. The proposed optimization strategies showcase the power of interdisciplinary approaches in advancing machine learning techniques for medical applications. The demonstrated improvements in classification metrics and computational efficiency highlight the model's potential for broader adoption in healthcare settings, providing a robust, fast, and more accurate tool for COVID-19 diagnosis.

Congruence between co-occurrence and trait-based networks is scale-dependent: a case study with flea parasites of small mammalian hosts.

We applied a novel framework based on network theory and a concept of modularity that estimates congruence between trait-based ( = functional) co-occurrence networks, thus allowing the inference of co-occurrence patterns and the determination of the predominant mechanism of community assembly. The aim was to investigate the relationships between species co-occurrence and trait similarity in flea communities at various scales (compound communities: across regions within a biogeographic realm or across sampling sites within a geographic region; component communities: across sampling sites within a geographic region; and infracommunities: within a sampling site). We found that compound communities within biogeographic realms were assembled via environmental or host-associated filtering. In contrast, functional and spatial/host-associated co-occurrence networks, at the scale of regional compound communities, mostly indicated either stochastic processes or the lack of dominance of any deterministic process. Analyses of congruence between functional and either spatial (for component communities) or host-associated (for infracommunities) co-occurrence networks demonstrated that assembly rules in these communities varied among host species. In component communities, stochastic processes prevailed, whereas environmental filtering was indicated in 4 and limiting similarity/competition in 9 of 31 communities. Limiting similarity/competition processes dominated in infracommunities, followed by stochastic mechanisms. We conclude that assembly processes in parasite communities are scale-dependent, with different mechanisms acting at different scales.

Open comments on Blok & Bruun Jensen's paper ‘What next for actor network theory? Inventing around Latour on a planet in distress’

My comment mostly underlines the inadequacy of the tone of the paper, very critical about the way Latour has been criticized on one side, and somehow dogmatic on the other, in the way the authors present. Latour's propositions, from Actor Network Theory (ANT) to the Inquiry into the Modes of Existence (IME), though himself conceived them as proposals to be discussed, modified, criticized, something himself and Callon had done very early about ANT, in 1999.

Longitudinal relations between non-suicidal self-injury and both depression and anxiety among senior high school adolescents: a cross-lagged panel network analysis.

Comorbidity between non-suicidal self-injury (NSSI) and depression and anxiety was common. In the framework of network theory, the examination of directionality and gender differences in longitudinal relationships at the symptom level made a significant contribution to the understanding of comorbidity. Therefore, this study employed cross-lagged panel network analysis to investigate the longitudinal interrelations between NSSI and depression and anxiety in Chinese adolescents, with a focus on gender differences. The study was conducted with a sample of 884 senior high school students (F/M: 481/403; mean age: 15.19±0.48 years) from Jinchang City, Gansu Province, China. All respondents completed the Adolescent Non-Suicidal Self-Injury Assessment Questionnaire and the two subscales (depression and anxiety) of the Brief Symptom Inventory at two intervals. The data were estimated in R 4.2.0 to construct the cross-lagged panel network (CLPN). The CLPN results uncovered the gender differences. For boys, self-hitting and feeling scared emerged as central symptoms. Cutting predicted subsequent feelings of sadness (β=-0.57), tension (β=-0.52) and indifference (β=-0.49), potentially serving as a bridge connecting NSSI to depression and anxiety. For girls, biting themselves and feeling scared were central symptoms. Carving and skin rubbing predicted subsequent feelings of indifference (β=-0.31, -0.21), bridging NSSI to depression and anxiety. In addition, feeling scared emerged as the key bridge symptom connecting depression and anxiety. The findings showed the gender-specific developmental characteristics of the directional relations between NSSI and depression and anxiety at the symptom level. They provided new insights into the comorbidity of NSSI and depression and anxiety, carrying important implications for the screening and intervention of adolescent NSSI.

What may be: policy enactment in education, a new conceptual framework with actor-network theory

ABSTRACT In this article, I present a new conceptual framework constructed using sensemaking theory and Actor–Network Theory (ANT) to demonstrate layers of policy enactment. The framework reimagines policy enactment as a sociomaterial ethico-political activity by considering the factors that mediate policy enactment and the assemblages facilitating the translation of policy into action. The framework forms an idealised cycle, using concepts from sensemaking theory including sensegiving and the system builder and Actor-Network Theory; problematisation, interessement, enrolment, mobilisation and stabilisation. This framework is then applied to research focussing on a forming Multi-Academy Trust and the attempt to establish joint 6th form provision. This highlights the potential human, contextual and material difficulties faced by schools as they attempt to translate national policy into practice and work together. The conceptual framework provides an alternative way of thinking about policy enactment, one that allows a reflection on the difficulties associated with translating policy into practice whilst also offering insights to help these to be overcome.

Construction of knowledge sharing network indicator system for medication therapy management service training teams based on social network analysis

BackgroundBased on the perspective of social network theory, this study explored the network indicator system that facilitated optimal knowledge sharing effect in Medication Therapy Management Services (MTMS) training teams. The aim was to provide a reference for optimizing MTMS training and improving training quality.MethodsUtilizing social network analysis combined with a questionnaire survey, a knowledge sharing matrix for MTMS training teams was constructed. Knowledge sharing behavior was assessed from three perspectives: individual networks, whole networks, and cohesive subgroups.ResultsIndividual network analysis showed that the knowledge sharing effect within the training team reached its peak when the out-degree centrality was ≥ 3.5, in-degree centrality was ≥ 2.5, eigenvector centrality was ≥ 0.065, and closeness centrality was ≥ 7.86. Whole network analysis indicated that the optimal knowledge sharing effect occurred when the network density of the training team was higher than 0.0343 and the training size was less than 117 participants. Cohesion subgroups analysis demonstrated that knowledge sharing was more effective when members with similar working years participated in training together.ConclusionsThe knowledge sharing indicator system developed for MTMS training teams, based on social network analysis, can assist in optimizing the MTMS training model and improving training effectiveness.

Network-Based Modeling of Lean Implementation Strategies and Planning in Prefabricated Construction

Abstract: Prefabricated construction (PC) is increasingly promoted in the construction sector for its potential benefits, including reduced resource assumption and improved quality. Accordingly, Lean methods are popularly applied to PC projects for optimizing operational processes and enhancing their performance in line with strategic objectives. A key factor in effectively implementing Lean to improve strategic control is developing specific strategies and planning that consider their complex interactions. Thus, this paper aims to propose a quantitative network-based model by integrating Interpretive Structural Modeling (ISM) and Matrix Impact Cross-Reference Multiplication Applied to a Classification (MICMAC) under complex network theory to develop a Lean implementation framework for effective strategy formulation. Specifically, 17 Lean implementation strategies for PC in the context of the Chinese prefabrication industry were identified via an extensive literature review and expert interviews. Then, ISM-MICMAC quantitatively identifies the direct and indirect relationships among strategies, while subsequent analysis of Topological Structure Weight (TSW) and Structural Degree Weight (SDW), as complex network parameters, is used to evaluate the importance of each strategy. The findings show that the strategic planning for Lean implementation in PC consists of four levels, i.e., foundation, organizational, technical, and control. Selecting appropriate Lean tools and technologies is crucial for PC implementation, which must be built on a top-level management team and foster a Lean culture. Moreover, it involves building a standardized system of processes and activities, enhancing both internal and external collaboration, and continuously improving processes in response to changes. On one hand, this in-depth network-based analysis offers practical insights for PC stakeholders, particularly in China, on Lean implementation in line with PC performance and strategic control and objectives. On the other hand, the network-based model can be future-implemented globally. Additionally, this study expands the current body of knowledge on Lean in PC by exploring the interrelationships of Lean implementation strategies.

The collusion behavior of venture capitalists and entrepreneurs based on “guanxi”: evidence from China

The role of venture capital in promoting entrepreneurship and funding innovation cannot be underestimated. However, it is important to acknowledge noteworthy concerns associated with collusive behavior in the venture capital process. “Guanxi” is a relatively intimate and closed social relationship, which can impact venture capital through trust and social obligations. Drawing on the theories of unethical pro-relational behavior and social networks, using data from GEM-listed companies supported by venture capital from 2009 to 2021, this research investigates the phenomenon of collusion behavior between venture capitalists and entrepreneurs, with a particular focus on the influence of “guanxi”. The research finds that the presence of “guanxi” between venture capitalists and entrepreneurs tends to facilitate collusive behavior. Specifically, “guanxi” tends to increase entrepreneurial self-interest behavior, and venture capitalists’ returns, infringe upon minority shareholders’ interests, and increase corporate operational risk. Moreover, the study further explores the moderator effect of equity incentives on such collusion behavior. This research not only enriches the theoretical framework of the relationship between “guanxi” and venture capital activities but also provides insight into the ethical implications associated with “guanxi”, contributing to the broader literature on social relationships and unethical behavior.

Energy storage configuration method for distribution networks based on moment difference analysis

The integration of a high proportion of distributed PV into distribution networks can cause power backfeeding and voltage limit violations. This paper introduces moment difference analysis theory for distribution networks, reframing PV consumption as the balancing moment difference equations. When maximum node voltage approaches the upper limit, the moment difference between PV and load stabilizes to an approximately constant, termed the standard moment difference. The standard moment difference represents the limit of the network's capacity to consume distributed PV. Essentially, the PV moment is the target for integration, while the load moment serves as the resource to consume the PV moment. Based on this theory, a method for energy storage configuration is proposed. Simplifying a complex multi-branch distribution network into single-branch lines and solving linear equations determines the optimal storage configuration. This method was tested on the Jibei Power Grid and the IEEE 33 and 69 bus systems, increasing computational efficiency by 1611.47–4973.82 times compared to the PSO algorithm, while maintaining an error margin of less than 2.6 %. Furthermore, the discharging strategy reduces the ESS capacity to between 12.39 % and 31.69 % of the original estimate, thereby enhancing both the economic and practical appeal of the approach.

The Role of Complex Systems in Predictive Analytics for E-Commerce Innovations in Business Management

This review explores the incorporation of complex systems theory into predictive analytics in the e-commerce sector, particularly emphasizing recent advancements in business management. By analyzing the intersection of these two domains, the review emphasizes the potential of complex systems models—including agent-based modeling and network theory—to improve the precision and efficacy of predictive analytics. It will provide a comprehensive overview of the applications of emergent predictive analytics techniques and tools, including real-time data analysis and machine learning, in inventory optimization, dynamic pricing, and personalization of customer experiences. In addition, this review will suggest future research directions to advance the discipline and address the technical, ethical, and practical challenges encountered during this integration phase.

Hierarchical structure-based model for importance and reliability assessment of water distribution networks

The Segment-Valve (SV) model can be utilized to analyze the reliability of water distribution networks (WDNs). However, it often focuses on the impact of segment failures on themselves. The isolation of segments in the WDNs not only affects the segments themselves but also influences other segments through which the water supply path passes. Therefore, considering the water supply path and the interaction between upstream and downstream segments, we convert the loops in the SV graph to nodes to simplify them, clearly describing the segmented hierarchy and its interconnections in a tree-like form, termed SV-tree. Based on the SV-tree and complex network theory, a method is proposed to estimate the supply shortage rate using betweenness centrality to provide a detailed analysis for local importance on example WDNs. Meanwhile, new analysis indicators that can reflect the global reliability of the WDNs are constructed from the mutual influence between segments and the difficulty for users to obtain water. The results demonstrate the efficacy of the new importance assessment indicator across various WDNs configurations, and its calculation time is much lower than that of hydraulic simulation. In addition, the reliability assessment indicators are more practical and can effectively identify problems existing in the WDNs.

Inverse system based decoupling design and control strategy for crude oil heat exchanger networks

This work focuses on a decoupling control approach for the crude oil heat exchanger network to improve the energy efficiency and achieve continuous energy saving. The desired control variables are identified by the signed directed graph (SDG) model based on the complex network theory. Then the principal component analysis (PCA) method and the evaluation index are employed to construct the initial controller as well as the control variables pairing. The presented system is a multi-input multi-output (MIMO) control system where the coupling problems are considered, and a decoupling control strategy based on the inverse system theory of neural network is proposed. The existence of the inverse system is proved by the reversibility derivation, and the original system is decoupled into two second-order subsystems. Consequently, the proposed decoupling control system is tested on the example of heat exchanger network (HEN) belonging to Crude Distillation Unit. The fluctuating value of temperature is reduced averagely by 57.1% compared with the non-decoupling control strategy and the mean absolute percentage error is within 0.02 under different operating conditions. The results show that the proposed decoupling control method has excellent decoupling performance and can improve the control performance of the system.

A Longitudinal Network Analysis of Emotion Regulation, Interpersonal Problems, and Eating Disorder Psychopathology in Chinese Adolescents.

The present longitudinal study examined sex-specific, symptom-level relationships among emotion regulation (ER), interpersonal problems (IP), and eating disorder (ED) psychopathology in a large sample of Chinese adolescents. Data were from a project with four waves of data collection (N = 1540; 710 boys and 830 girls) at 6-month intervals over 18 months. Questionnaires assessed ED psychopathology, ER, and IP at each wave of data collection. Longitudinal network analyses were conducted separately for boys and girls. Sex differences in the network structures were also examined. The results revealed pronounced heterogeneity in the presentation of ED psychopathology, ER, and IP across Chinese adolescent boys and girls longitudinally and intra-individually. For example, weight/shape preoccupation in ED psychopathology and awareness in ER emerged as important nodes in the temporal network for boys. However, weight/shape preoccupation and dissatisfaction in ED psychopathology were identified as the most important nodes in the temporal network for girls. Regarding bridge strength, awareness in ER emerged as the node with the highest connectivity in the temporal network for boys. At the same time, weight/shape dissatisfaction in ED psychopathology was the node with the highest connectivity for girls. The current study extended network theory to better understand the longitudinal interplay among ER, IP, and ED psychopathology in Chinese adolescents and their sex differences in the importance of symptoms. Such insights may pave the way for developing targeted prevention and treatment strategies for adolescent boys and girls in China.