In recent years, the method of the complex network has been applied to various fields. Dynamics research in complex networks is also an important branch. There are many types of research into dynamic complex network, but few scholars use bibliometrics to study it. Therefore, this paper adopts the method of bibliometrics to analyze the development history and status quo of dynamic complex network, providing a summary description of this research field. We used CiteSpace and Pajek (...) to analyze 2936 relevant papers on the website of Web of Science and further interpreted and summarized the analysis results. To fully show the bibliometric results, we further derive the productive countries, institutions, sources, and authors and some main research directions in the dynamic complex network research. The research results show that the dynamic complex network research method was applied to various fields after the dynamic research method had been integrated into the complex network. According to the subject analysis of the cited papers, we find that the dynamic complex network method has been widely used in various subjects. Through emergent analysis, we found that the latest hot research trends are the study of infectious diseases and applications in neural networks. At the same time, through the main path analysis, we find the essential literature to elaborate on the development context of a dynamic complex network at different time points. This paper gives a comprehensive analysis of dynamic complex network research and provides some enlightenment for the future research direction. (shrink)

Internet services, such as review sites, FAQ sites, online auction sites, online flea markets, and social networking services, are essential to our daily lives. Each Internet service aims to promote information exchange among people who share common interests, activities, or goods. Internet service providers aim to have users of their services actively communicate through their services. Without active interaction, the service falls into disuse. In this study, we consider that an Internet service has a network externality as its main (...) feature, and we model user behavior in the Internet service with network externality as a dynamic game. In particular, we model the diffusion process of users of an ISNE as an infinite-horizon extensive-form game of complete information in which: each user can choose whether or not to use the ISNE in her/his turn and the network effect of the ISNE depends on the history of each player’s actions. We then apply Markov perfect equilibrium to analyze how to increase the number of active users. We derive the necessary and sufficient condition under which the state in which every player is an active user is the unique Markov perfect equilibrium outcome. Moreover, we propose an incentive mechanism that enables the number of active users to increase steadily. (shrink)

Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining (...) the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments. (shrink)

What I call "strategic injustice" involves a set of formal and informal regulatory rules and conventions that often lead to grossly unfair outcomes for a class of individuals despite their resistance. My goal in this paper is to provide the necessary conditions for such injustices and for eliminating their instances from our social practices. To do so, I follow Peter Vanderschraaf's analysis of circumstances of justice and expand his account by embedding "asymmetric conflictual coordination games" that summarize fair division (...) problems in a dynamic social network. I use the network effect on such coordination games to explain the emergence of stable exploitative behavior and conventions by a class of individuals even in the presence of restraining efforts by others. I conclude that such unfair conventions are resilient to uncoordinated individual actions and interventions. In fact, maintaining a rough equality itself turns into another coordination problem. Finally, I show that something similar to a social movement that restructures the network of social relations is necessary to solve such coordination problems. (shrink)

Mechanist philosophers have examined several strategies scientists use for discovering causal mechanisms in neuroscience. Findings about the anatomical organization of the brain play a central role in several such strategies. Little attention has been paid, however, to the use of network analysis and causal modeling techniques for mechanism discovery. In particular, mechanist philosophers have not explored whether and how these strategies incorporate information about the anatomical organization of the brain. This paper clarifies these issues in the light of (...) the distinction between structural, functional and effective connectivity. Specifically, we examine two quantitative strategies currently used for causal discovery from functional neuroimaging data: dynamic causal modeling and probabilistic graphical modeling. We show that dynamic causal modeling uses findings about the brain’s anatomical organization to improve the statistical estimation of parameters in an already specified causal model of the target brain mechanism. Probabilistic graphical modeling, in contrast, makes no appeal to the brain’s anatomical organization, but lays bare the conditions under which correlational data suffice to license reliable inferences about the causal organization of a target brain mechanism. The question of whether findings about the anatomical organization of the brain can and should constrain the inference of causal networks remains open, but we show how the tools supplied by graphical modeling methods help to address it. (shrink)

Agri-food clusters have generated great interest in recent years and prompted a new wave of research dedicated to ‘Localized Agri-Food Systems’. However, the specific nature of relations between firms who belong to SYALs has rarely been studied. Our purpose is to show how the analysis of company directors’ advice networks helps to better understand the specificity and innovative dynamics of SYALs. Our research was based on a case study in the Biterrois wine growing region of southern France. We conducted (...) a survey to collect information on advice relations between wine cooperative managers, who play a key role in the innovation process toward quality wine. Sociometric analysis of the advice relations, coupled with interviews, led to three main results: the first was that managers simultaneously cooperate on technical projects and compete for business ends. The second was a correlation between the innovations implemented by the cooperatives and the degree of centrality of the managers in the networks, revealing the relational and local conditions of innovations in SYALs. The third was that managers can modify their own network to improve the competitiveness of both their own cooperative and of the SYAL as a whole. In conclusion, we show that advice network analysis is an appropriate tool to describe the relational dimension of SYALs, to better understand their process of innovation, and to help managers improve the collective strategies they apply within the SYAL. (shrink)

As one of the key subjects of multi-center governance of environmental concerns, public perception is crucial in forming and implementing environmental policy. Based on data science research theory and the original theory of public perception, this study proposes a research framework to analyze environmental policy through network text analysis. The primary contents are bidirectional encoder representation from transformers-convolution neural network sentiment tendency analysis, word frequency characteristic analysis, and semantic network analysis. The realism of (...) the suggested framework is demonstrated by using the waste classification policy as an example. The findings indicate a substantial relationship between perceived subject participation and policy pilot areas and that perceived subject participation is repeating. On this premise, specific recommendations are made to encourage policy implementation. (shrink)

The concept of attractors is considered critical in the study of dynamical systems as they represent the set of states that a system gravitates toward. However, it is generally difficult to analyze attractors in complex systems due to multiple reasons including chaos, high-dimensionality, and stochasticity. This paper explores a novel approach to analyzing attractors in complex systems by utilizing networks to represent phase spaces. We accomplish this by discretizing phase space and defining node associations with attractors by finding sink strongly (...) connected components within these networks. Moreover, the network representation of phase space facilitates the use of well-established techniques of network analysis to study the phase space of a complex system. We show the latter by introducing a new node-based metric called attractivity which can be used in conjunction with the SSCC as they are highly correlated. We demonstrate the proposed method by applying it to several chaotic dynamical systems and a large-scale agent-based social simulation model. (shrink)

BackgroundThe neural activity and functional networks of emotion-based cognitive reappraisal have been widely investigated using electroencephalography and functional magnetic resonance imaging. However, single-mode neuroimaging techniques are limited in exploring the regulation process with high temporal and spatial resolution.ObjectivesWe proposed a source localization method with multimodal integration of EEG and fMRI and tested it in the source-level functional network analysis of emotion cognitive reappraisal.MethodsEEG and fMRI data were simultaneously recorded when 15 subjects were performing the emotional cognitive reappraisal task. (...) Fused priori weighted minimum norm estimation with sliding windows was proposed to trace the dynamics of EEG source activities, and the phase lag index was used to construct the functional brain network associated with the process of downregulating negative affect using the reappraisal strategy.ResultsThe functional networks were constructed with the measure of PLI, in which the important regions were indicated. In the gamma band source-level network analysis, the cuneus, the lateral orbitofrontal cortex, the superior parietal cortex, the postcentral gyrus, and the pars opercularis were identified as important regions in reappraisal with high betweenness centrality.ConclusionThe proposed multimodal integration method for source localization identified the key cortices involved in emotion regulation, and the network analysis demonstrated the important brain regions involved in the cognitive control of reappraisal. It shows promise in the utility in the clinical setting for affective disorders. (shrink)

A neural network is a model of the brain’s cognitive process, with a highly interconnected multiprocessor architecture. The neural network has incredible potential, in the view of these artificial neural networks inherently having good learning capabilities and the ability to learn different input features. Based on this, this paper proposes a new chaotic neuron model and a new chaotic neural network model. It includes a linear matrix, a sine function, and a chaotic neural network composed of (...) three chaotic neurons. One of the chaotic neurons is affected by the sine function. The network has rich chaotic dynamics and can produce multiscroll hidden chaotic attractors. This paper studied its dynamic behaviors, including bifurcation behavior, Lyapunov exponent, Poincaré surface of section, and basins of attraction. In the process of analyzing the bifurcation and the basins of attraction, it was found that the network demonstrated hidden bifurcation phenomena, and the relevant properties of the basins of attraction were obtained. Thereafter, a chaotic neural network was implemented by using FPGA, and the experiment proved that the theoretical analysis results and FPGA implementation were consistent with each other. Finally, an energy function was constructed to optimize the calculation based on the CNN in order to provide a new approach to solve the TSP problem. (shrink)

Improving the resilience of urban road networks suffering from various disruptions has been a central focus for urban emergence management. However, to date the effective methods which may mitigate the negative impacts caused by the disruptions, such as road accidents and natural disasters, on urban road networks is highly insufficient. This study proposes a novel adaptive signal control strategy based on a doubly dynamic learning framework, which consists of deep reinforcement learning and day-to-day traffic dynamic learning, to improve (...) the network performance by adjusting red/green time split. In this study, red time split is regarded as extra traffic flow to discourage drivers to use affected roads, so as to reduce congestion and improve the resilience when urban road networks are subject to different levels of disruptions. In addition, we utilize the convolution neural network as Q-network to approximate Q values, link flow distribution and link capacity are regarded as the state space, and actions are denoted as red/green time split. A small network is utilized as a numerical example, and a fixed time signal control and other two adaptive signal controls are employed for the comparisons with the proposed one. The results show that the proposed adaptive signal control based on deep reinforcement learning can achieve better resilience in most of the cases, particularly in the scenarios of moderate and severe disruptions. This study may shed light on the advantages of the proposed adaptive signal control dealing with major emergencies compared to others. (shrink)

This study concentrates on adaptive event-triggered control of complex dynamical networks with unpredictable coupling delays and stochastic deception attacks. The adaptive event-triggered mechanism is used to avoid the wasting of limited bandwidth. The probability of data communicated by the network is established by statistical properties and Bernoulli stochastic variables with an uncertain occurrence probability. Stability analysis based on Lyapunov–Krasovskii functional and the stability of the closed-loop system is guaranteed. Using the LMI technique, we obtain triggered parameters. To demonstrate (...) the feasibility and usefulness of the suggested methodology, two examples are shown. (shrink)

The dynamic models are proposed to investigate the influence node activity has on rumor spreading process in both homogeneous and heterogeneous networks. Different from previous studies, we believe that the activity of nodes in complex networks affects the process of rumor spreading. An active node can have contact with all the nodes it directly links to, while an inactive node could only interact with its active neighbors. We explore the joint effort of activity rate, spreading rate and network (...) topology on rumor spreading process by mean-field equations and numerical simulations, which reveals that there exists a critical curve consisting of critical activity rate and spreading rate; meanwhile, activity rate and spreading rate both have influence on the final rumor spreading scale. (shrink)

In this paper, we analyze and calculate the crowd density in a tourist area utilizing video surveillance dynamic information analysis and divide the crowd counting and density estimation task into three stages. In this paper, novel scale perception module and inverse scale perception module are designed to further facilitate the mining of multiscale information by the counting model; the main function of the third stage is to generate the population distribution density map, which mainly consists of three columns (...) of void convolution with different void rates and generates the final population distribution density map using the feature maps of different branch regressions. Also, the algorithm uses jump connections between the top convolution and the bottom void convolution layers to reduce the risk of network gradient disappearance and gradient explosion and optimizes the network parameters using an intermediate supervision strategy. The hierarchical density estimator uses a hierarchical strategy to mine semantic features and multiscale information in a coarse-to-fine manner, and this is used to solve the problem of scale variation and perspective distortion. Also, considering that the background noise affects the quality of the generated density map, the soft attention mechanism is integrated into the model to stretch the distance between the foreground and background to further improve the quality of the density map. Also, inspired by multitask learning, this paper embeds an auxiliary count classifier in the count model to perform the count classification auxiliary task and to increase the model’s ability to express semantic information. Numerous experimental results demonstrate the effectiveness and feasibility of the proposed algorithm in solving the problems of scale variation and perspective distortion. (shrink)

Social network analysis has gained increasing academic attention in the construction domain over the past two decades due to its capability to characterize the complexity and dynamics of interindividual and interorganizational interactions. To date, however, scant attempt has been made to develop an integrated framework to systematically review the diversified network research at different levels in this domain and to quantitatively characterize the evolution of related research interests and research instruments. This study aims to fill this gap (...) by conducting a bibliometric-qualitative review based on 106 papers published from 1997 to 2020. Keyword cooccurrence analysis is employed to reveal the research foci, identify the research trends, and develop a comprehensive categorization framework, which classifies related research based on two interrelated dimensions: the type of network node and the levelof network analysis. The framework then facilitates further content analysis in terms of research topics, research designs, and research instruments. The results provide evidence that the research foci in this domain are generally moving towards addressing the complexity and dynamics of project-related relations at more diversified levels, in terms of not only research topics but also research instruments. Future research can be enriched by investigating the multiple types of dynamic interproject relationships, adopting state-of-the-art methodologies for network data collection and triangulation, and employing multiple SNA constructs and inferential statistical methods to reveal how complex networks coevolve and interact with actors’ behaviors as well as project and organizational outcomes. (shrink)

Through structural analysis of 8-year tag networks from online knowledge-sharing platforms, this study finds that, with the scale of tag networks growing quickly, the growth trend of number edges indicates that tag network follows densification law. The clustering coefficient and the average shortest path of the network show that the rapid growth of network size does not bring about the compartmentalization of the knowledge network, and the degree distribution of tag networks shows a truncated power-law (...) distribution. According to the structural characteristics of the tag network, this study proposes a tag network model based on the BA model. Based on the preference attachment, the triadic closure mechanism is employed to construct the edges between the old nodes, which revises the limitation that the BA model only connects edges between old and new nodes. The results show that the simulation model matches the actual tag network structure well. The generation mechanism of the tag network model provides a reference for understanding the knowledge construction process of the online knowledge-sharing platform to a certain extent. (shrink)

I explored the relationship between the “Black Axe” Confraternity and cybercrime, with a particular emphasis on the structural dynamics of the Business Email Compromise (BEC) schemes. I investigated whether a conventional hierarchical system governs the membership and remuneration for BEC roles as perpetrators by interviewing an accused “leader” of the “Black Axe” affiliated cybercriminal incarcerated in a prominent Western nation. I supplemented the analysis of interview data with insights from tapped phone records monitored by a law enforcement entity. I (...) merged Actor-network theory and Social Network theory as analytical frameworks and thematically analyzed data to produce six overarching themes: (1) The fluidity of Structure and Adaptive Roles in BEC, (2) Challenges in Visualizing Criminal Networks, (3) Globalization and Transnational Dimensions, (4) Social and Cultural Influences, (5) Internal Cybersecurity Threats and Money Laundering, and (6) Remuneration and Influences of Cryptocurrency, casting a brighter light on the topic. Unlike traditional organized crime, BEC scammers have adopted a nonhierarchical model that is flexible and fluid. I found no evidence of a rigid hierarchy dictating positions and remuneration for BEC roles. Instead, I observed that cybercriminals involved in BEC activities functioned horizontally, promoting fluidity, maneuverability, collaboration, and specialization across various facets of their illicit pursuits. I also highlighted the cross-border links BEC offenders have with other criminal actors facilitated by Black Axe organizational machinery. My research adds value to the existing body of knowledge, utilizing a unique dataset comprising direct testimonies of a high-profile BEC offender affiliated with the ‘hard–to–access,’ Black Axe gang. (shrink)

During the last decade, Network Science has become one of the most active fields in applied physics and mathematics, since it allows the analysis of a diversity of social, biological and technological systems [24]. From the diversity of applications of Network Science, in this Opinion paper we are concerned about its potential to analyse one of the most extended group sports, Football (soccer in U.S. terminology) [29], since it allows addressing different aspects of the team organization and (...) performance not captured by classical analyses based on the performance of individual players. The reason behind relies on the complex nature of the game, which, paraphrasing the foundational paradigm of complexity sciences “can not be analysed by looking at its components (i.e., players) individually but, on the contrary, considering the system as a whole” or, in the classical words of after-match interviews “it’s not just me, it’s the team”. The recent ability of obtaining datasets of all events occurring during a match, including the position of the players and the interactions between them has opened the door to new kind of studies where it is possible to analyse and quantify the behaviour of a team as a whole, together with the role of a single player [17]. Under this framework, the organization of a team can be considered as the result of the interaction between its players, creating a network based on passes. In this way, we can create passing networks, which are directed (i.e.,... (shrink)

This paper makes a quantitative analysis of the comprehensive influence of music networks. Firstly, 11 music features are selected from energy, popularity, and other aspects to build a comprehensive evaluation index of music influence, and the PageRank algorithm is used to quantify the music influence. Secondly, the multiobjective logistic regression is used to construct the music similarity measurement model and, combined with music influence and music similarity, to judge whether the influence of different musicians is the actual influence. Thirdly, (...) the influence and similarity of the same music genre and different music genres are analyzed by using the two-way cluster analysis method. Finally, the lasso region is used for feature selection to obtain the change factors in the process of music evolution and analyze the dynamic changes in the process of music development. Therefore, this paper uses network science to build a dynamic network to analyze the similarity of music, the evolution process, and the impact of music on culture, which has certain research significance and practical value in the fields of music, history, social science, and practice. (shrink)

With the rapid development of social network in recent years, the threshold of information dissemination has become lower. Most of the time, rumors, as a special kind of information, are harmful to society. And once the rumor appears, the truth will follow. Considering that the rumor and truth compete with each other like light and darkness in reality, in this paper, we study a rumor spreading model in the homogeneous network called 2SIH2R, in which there are both spreader1 (...) and spreader2. In this model, we introduced discernible mechanism and confrontation mechanism to quantify the level of people's cognitive abilities and the competition between the rumor and truth. By mean-field equations, steady-state analysis, and numerical simulations in a generated network which is closed and homogeneous, some significant results can be given: the higher the discernible rate of the rumor, the smaller the influence of the rumor; the stronger the confrontation degree of the rumor, the smaller the influence of the rumor; the larger the average degree of the network, the greater the influence of the rumor but the shorter the duration. The model and simulation results provide a quantitative reference for revealing and controlling the spread of the rumor. (shrink)

The paper focuses on knowledge generation, a topic frequently overlooked in the traditional debates in epistemology and philosophy of science. We focus on investigation as the primary process generating knowledge and its products. Investigation is taken as a generalization of the research process that includes similar knowledge-generating practices in aboriginal communities. To characterize the complexity of investigation processes and their products we go beyond traditional epistemological characterization of knowledge in terms of mental states and turn to the concept of routine. (...) Investigation processes share a common symbolic representation form which we call here a knowledge thread. The dynamics of the knowledge thread may be characterized by two intertwined tiers: cognitive and institutional. Using examples from our previous studies, especially on the recent discovery of microRNAs in molecular biology, we illustrate the dynamics of threads and claim that it is susceptible to the social network analysis which, however, requires insightful applications and sound interpretation of the results. Such an interpretation, as we suggest, may further be elaborated on the grounds of anthropological theories of distributed agency and distributed cognition. (shrink)

In the real world, individual resources are crucial for patients when epidemics outbreak. Thus, the coupled dynamics of resource diffusion and epidemic spreading have been widely investigated when the recovery of diseases significantly depends on the resources from neighbors in static social networks. However, the social relationships of individuals are time-varying, which affects such coupled dynamics. For that, we propose a coupled resource-epidemic dynamic model on a time-varying multiplex network to synchronously simulate the resource diffusion and epidemic spreading (...) in dynamic social networks. The equilibrium analysis of the coupled model is conducted in a general scenario where the resource generation varies between susceptible and infected states and the recovery rate changes between resourceful and noresource states. By using the microscopic Markov chain approach and Monte Carlo simulations, we determine a probabilistic framework of the intralayer and interlayer dynamic processes of the coupled model and obtain the outbreak threshold of epidemic spreading. Meanwhile, the experimental results show the trivially asymmetric interactions between resource diffusion and epidemic spreading. They also indicate that the stronger activity heterogeneity and the larger contact capacity of individuals in the resource layer can more greatly promote resource diffusion, effectively suppressing epidemic spreading. However, these two individual characters in the epidemic layer can cause more resource depletion, which greatly promotes epidemic spreading. Furthermore, we also find that the contact capacity finitely impacts the coupled dynamics of resource diffusion and epidemic spreading. (shrink)

This paper introduces a logic to reason about a well-known model of opinion dynamics in socialnetworks initially developed by Morris DeGroot as well as Keith Lehrer and Carl Wagner. The proposed logic is an extension of Lukasiewicz' famous fuzzy logic with additional equational expressivity, modal operators, machinery from hybrid logic, and dynamic modalities. The model of opinion dynamics in social networks is simple enough to be easily grasped, but still complex enough to have interesting mathematical properties and applications. Thus, (...) developing a logic to reason about this particular model serves as a paradigmatic example of how logic can be useful in social network analysis. (shrink)

Air transport is playing an increasing role in the world economy every year. This is facilitated by technological development and the latest developments in the aviation industry, globalization. This paper provides an overview of artificial neural network training methods for airfare predicting. The articles for 2017-2019 were analyzed in order to determine the model with the most accurate prediction. The researchers conducted research on open data collected by themselves and set themselves the goal of creating a model that would (...) advise a user the best time to buy a ticket when the price would be the lowest. The review of the papers by similar themes revealed that the Bagging Regression Tree model has the highest results with an accuracy of 88% and the random forest method has an accuracy of 87%. Civil aviation plays an important role in the economy of each country. Aviation is the best way to cover long distances in comfort in the shortest time. Airlines offer customers a variety of opportunities to travel both within the country and abroad. The main problem of interaction between airlines and customers is the airfare: the former want to sell more at the higher price, and the latter want to buy cheaper. Therefore, companies use their own private algorithms for dynamic pricing and constantly monitor the market situation, responsive to changes in demand and the actions of competitors. This behavior allows them to achieve a balance between the desires of airlines and customers. Scientists are trying to invent a way to predict airfare so that air travelers can buy them at the lowest price. The results of the work in this area provide general rules for the best purchase. For example, according to the article thebest day to buy a ticket by expedia.com for a domestic flight on the United States is Sunday, and the best period is 57 days before departure. This article provides an overview of the works, the authors of which compared the models of machine learning. Achievements in this area are limited to direct flights of a certain domestic market and 88% accuracy of the forecast. The Bagging Regression Tree model described in the article can be considered the best result. This trained model can make predictions based only on two parameters: the number of free cargo and the number of days left before departure and has an accuracy of 88%. (shrink)

Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved medium (...) traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole. (shrink)

The spread of unethical behavior in organizations has mainly been studied in terms of processes occurring in a general social context, rather than in terms of actors’ reactions in the context of their specific social relationships. This paper introduces a dynamic social network analysis framework in which this spread is conceptualized as the result of the reactions of perpetrators, victims, and observers to an initial act of unethical behavior. This theoretical framework shows that the social relationships of (...) the actors involved in an initial act impact in multiple ways the likelihood that unethical behavior spreads. It reveals furthermore that social relationships may change in the wake of unethical behavior, such that indirect negative consequences can arise for organizations. The proposed framework provides a basis for the development of a formal stochastic actor-oriented model of network dynamics which would enable simulations of the spread of unethical behavior. (shrink)

This research concentrates on the design and analysis of an algorithm referred to as Virtual Network Configuration (VNC) which uses predicted future states of a system for faster network configuration and management. VNC is applied to the configuration of a wireless mobile ATM network. VNC is built on techniques from parallel discrete event simulation merged with constraints from real-time systems and applied to mobile ATM configuration and handoff. Configuration in a mobile network is a (...) class='Hi'>dynamic and continuous process. Factors such as load, distance, capacity and topology are all constantly changing in a mobile environment. The VNC algorithm anticipates configuration changes and speeds the reconfiguration process by pre-computing and caching results. VNC propagates local prediction results throughout the VNC enhanced system. The Global Positioning System is an enabling technology for the use of VNC in mobile networks because it provides location information and accurate time for each node. This research has resulted in well defined structures for the encapsulation of physical processes within Logical Processes and a generic library for enhancing a system with VNC. Enhancing an existing system with VNC is straight forward assuming the existing physical processes do not have side effects. The benefit of prediction is gained at the cost of additional traffic and processing. This research includes an analysis of VNC and suggestions for optimization of the VNC algorithm and its parameters. (shrink)

The global environmental and social-economic crises of industrialized agriculture have led to the emergence of agroecology as an alternative approach aiming to increase the ecological, social and economic sustainability of agri–food systems. The ‘multi-level perspective’ is now a widely used framework to understand and promote the upscaling of local innovation niches, such as agroecology, to broader scales, thus reconfiguring the dominant socio-technical regimes. Additionally, emergent ‘hybrid forums’ can provide a space between niche and regime where niche innovators can become important (...) actors in scaling up and out emergent innovations. In this paper, we examine a university training program, to better understand its role as a ‘hybrid forum’. Our analysis focuses especially on how the program, as an example of a hybrid forum, worked to reconfigure practices, concepts, and tools of local development practitioners. We also assess to what extent the program contributed to transitioning local development institutions toward agroecology. An online survey and in-depth interviews were carried out to determine how the training program has impacted the student’s opinions and their respective institutions. The results show that most of the students consider that they have acquired new theoretical frameworks and useful methods to re-framing their local development projects, that new alliances with multi-actor networks have been perceived, and that some internal changes of the local development practices have taken place. We conclude that the training program, as a hybrid forum, is capable of outscaling niche innovations through linkages with different kind of actors both from the niche and the regime. Political changes in the socio-technical landscape level offer an opportunity to amplify the impact of the innovations which are being generated by those multi-actor networks, but with a limited multi-level impact as far as institutional regime-actors not aligned with agroecological transition keep the most of the competencies on agri–food systems. (shrink)

The global environmental and social-economic crises of industrialized agriculture have led to the emergence of agroecology as an alternative approach aiming to increase the ecological, social and economic sustainability of agri–food systems. The ‘multi-level perspective’ is now a widely used framework to understand and promote the upscaling of local innovation niches, such as agroecology, to broader scales (e.g., regional, national, international), thus reconfiguring the dominant socio-technical regimes. Additionally, emergent ‘hybrid forums’ can provide a space between niche and regime where niche (...) innovators can become important actors in scaling up and out emergent innovations. In this paper, we examine a university training program (Postgraduate Diploma in Local Agroecological Dynamization at the Universitat Autònoma de Barcelona), to better understand its role as a ‘hybrid forum’. Our analysis focuses especially on how the program, as an example of a hybrid forum, worked to reconfigure practices, concepts, and tools of local development practitioners. We also assess to what extent the program contributed to transitioning local development institutions toward agroecology. An online survey (n = 46) and in-depth interviews (n = 16) were carried out to determine how the training program has impacted the student’s opinions and their respective institutions. The results show that most of the students consider that they have acquired new theoretical frameworks and useful methods to re-framing their local development projects, that new alliances with multi-actor networks have been perceived, and that some internal changes of the local development practices have taken place. We conclude that the training program, as a hybrid forum, is capable of outscaling niche innovations through linkages with different kind of actors both from the niche and the regime. Political changes in the socio-technical landscape level offer an opportunity to amplify the impact of the innovations which are being generated by those multi-actor networks, but with a limited multi-level impact as far as institutional regime-actors not aligned with agroecological transition keep the most of the competencies on agri–food systems. (shrink)

ObjectiveStatic regional homogeneity based on the resting-state functional magnetic resonance imaging has been used to study intrinsic brain activity in Alzheimer’s disease. However, few studies have examined dynamic ReHo in AD. In this study, we used rs-fMRI and dReHo to investigate the alterations in dynamic IBA in patients with AD to uncover dynamic imaging markers of AD.MethodIn total, 111 patients with AD, 29 patients with mild cognitive impairment, and 73 healthy controls were recruited for this study ultimately. (...) After the rs-fMRI scan, we calculated the dReHo values using the sliding window method. ANOVA and post hoc two-sample t-tests were used to detect the differences among the three groups. We used the mini-mental state examination and Montreal Cognitive Assessment to evaluate the cognitive function of the subjects. The associations between the MMSE score, MoCA score, and dReHo were assessed by the Pearson correlation analysis.ResultsSignificant dReHo variability in the right middle frontal gyrus and right posterior cingulate gyrus was detected in the three groups through ANOVA. In post hoc analysis, the AD group exhibited significantly greater dReHo variability in the right MFG than the MCI group. Compared with the HC group, the AD group exhibited significantly increased dReHo variability in the right PCG. Furthermore, dReHo variability in the right PCG was significantly negatively correlated with the MMSE and MoCA scores of patients with AD.ConclusionDisrupted dynamic IBA in the DMN might be an important characteristic of AD and could be a potential biomarker for the diagnosis or prognosis of AD. (shrink)

Borsboom and colleagues have recently proposed a “network theory” of psychiatric disorders that conceptualizes psychiatric disorders as relatively stable networks of causally interacting symptoms. They have also claimed that the network theory should include non-symptom variables such as environmental factors. How are environmental factors incorporated in the network theory, and what kind of explanations of psychiatric disorders can such an “extended” network theory provide? The aim of this article is to critically examine what explanatory strategies the (...) network theory that includes both symptoms and environmental factors can accommodate. We first analyze how proponents of the network theory conceptualize the relations between symptoms and between symptoms and environmental factors. Their claims suggest that the network theory could provide insight into the causal mechanisms underlying psychiatric disorders. We assess these claims in light of network analysis, Woodward’s interventionist theory, and mechanistic explanation, and show that they can only be satisfied with additional assumptions and requirements. Then, we examine their claim that network characteristics may explain the dynamics of psychiatric disorders by means of a topological explanatory strategy. We argue that the network theory could accommodate topological explanations of symptom networks, but we also point out that this poses some difficulties. Finally, we suggest that a multilayer network account of psychiatric disorders might allow for the integration of symptoms and non-symptom factors related to psychiatric disorders and could accommodate both causal/mechanistic and topological explanations. (shrink)

Farmers’ markets (FMs) represent a crucial player in urban food systems, being the interconnection of local agricultural production and consumption, and serving as spaces for both economic exchange and community building. Despite their transformative potential, there is a scarcity of research that comprehensively investigates the dynamics of FMs network structure and the influence of the actors shaping FMs. The present article delves into the network of relationships within FMs in the Italian city of Bologna. This study adopts the (...) Social Network Analysis (SNA) methodology applied with the Net-Map tool. The research objectives are to visualise the underlying network structure, map the dynamics, and identify the key actors who play pivotal roles in Bologna’s FMs and their level of influence. The research carried out interviews with ten FMs stakeholders, revealing the network of relationships between 54 actors, divided into three categories—civil society, food economy and public administration. Actors were linked by a total of 428 relationships across three types of networks: support, economic, and hindering. Findings indicate a strong support network structure characterised by varying degrees of centrality among different actors. Farmers emerge as a central node due to their pivotal role in providing fresh, local produce. Additionally, local institutions contribute significantly to FMs resilience and growth. Our research demonstrates the importance of recognising the embeddedness of FMs within the local context. By understanding the network structure and influential actors in FMs, policymakers can devise more effective policies for promoting local agriculture, and enhancing the sustainability of urban–rural exchanges. In conclusion, the present study offers valuable insights into the network dynamics of FMs, highlighting their crucial role in the sustainable development of urban and local food systems. (shrink)

Autism spectrum disorder is a group of complex neurodevelopment disorders characterized by altered brain connectivity. However, the majority of neuroimaging studies for ASD focus on the static pattern of brain function and largely neglect brain activity dynamics, which might provide deeper insight into the underlying mechanism of brain functions for ASD. Therefore, we proposed a framework with Hidden Markov Model analysis for resting-state functional MRI from a large multicenter dataset of 507 male subjects. Specifically, the 507 subjects included 209 (...) subjects with ASD and 298 well-matched health controls across 14 sites from the Autism Brain Imaging Data Exchange. Based on the HMM, we can identify the recurring brain function networks over time across ASD and healthy controls. Then we assessed the dynamical configuration of the whole-brain networks and further analyzed the community structure of transitions across the brain states. Based on the 19 HMM states, we found that the global temporal statistics of the specific HMM states were significantly altered in ASD compared to HCs. These specific HMM states were characterized by the activation pattern of default mode network, sensory processing networks [including visual network, auditory network, and sensory and motor network ]. Meanwhile, we also find that the specific modules of transitions between states were closely related to ASD. Our findings indicate the temporal reconfiguration of the brain network in ASD and provide novel insights into the dynamics of the whole-brain networks for ASD. (shrink)

The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case (...) studies. Our examples span from the investigation of organizational properties of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological complexity. (shrink)

This article correlates fine-grained semantic variability and change with measures of occurrence frequency to investigate whether a word’s degree of semantic change is sensitive to how often it is used. We show that this sensitivity can be detected within a short time span (i.e., 20 years), basing our analysis on a large corpus of German allowing for a high temporal resolution (i.e., per month). We measure semantic variability and change with the help of local semantic networks, combining elements of (...) deep learning methodology and graph theory. Our micro-scale analysis complements previous macro-scale studies from the field of natural language processing, corroborating the finding that high token frequency has a negative effect on the degree of semantic change in a lexical item. We relate this relationship to the role of exemplars for establishing form–function pairings between words and their habitual usage contexts. (shrink)

This article offers a critical discussion in the form of debate among experts in the fields of networks, human behaviour, and social analysis about key issues that arguably affect the human nature and society in the digital age. Based on the responses of Nicholas Christakis to an interview given to the authors, some key questions, applications, and limitations regarding the research on digital networks are discussed, together with hot issues related to the nature of digital data and experimentation in (...) contemporary social science. Finally, current and future prospects are presented in relation to data science and society in the light of the Fourth Industrial Revolution. (shrink)

With the emergence of participative social media, the ways in which stakeholders may interact with companies are changing. Social media and Web 2.0 technologies change gatekeeping mechanisms and the distribution of information. In consequence, organizations must realize that they are structurally embedded in online networks of interconnected and equitable actors. In this paper, we analyze how this change in today’s information and communication technologies may affect Corporate Social Responsibility action. We utilize social network analysis to investigate the CSR (...) blogs of three IT firms: Google, Hewlett-Packard, and Intel. The analysis reveals that their Internet-enabled social networks exhibit patterns of power law distribution and an uneven distribution of structural social capital among the actors involved, especially on the corporate side, which fails to fully engage with the network. We conclude by indicating the research implications of shifting social capital dynamics and by deriving implications for management and practice. (shrink)

The approach of the complex network has well described seismic complex systems. In this paper, this is the first time three classical network construction methods for seismicity are compared. By using the same dataset from the Southern California Seismic Network, three networks are constructed. They all present the scale-free, small-world properties, a strength-degree correlation, and an assortative mixing feature. However, they show some differences in the hierarchical clustering feature. On observing the evolution results, three measures show a (...) similar correlation with seismicity dynamics, but one measure shows a different result. These results show that different network construction methods will present some similarities and differences in network properties. This situation needs to be considered, especially when discussing a predictive indicator of seismicity. (shrink)

In this article, an in-depth study and analysis of economic structure are carried out using a neural network fusion release algorithm. The method system defines the weight space and structure space of neural networks from the perspective of optimization theory, proposes a bionic optimization algorithm under the weight space and structure space, and establishes a neuroevolutionary method with shallow neural network and deep neural network as the research objects. In the shallow neuroevolutionary, the improved genetic algorithm (...) based on elite heuristic operation and migration strategy and the improved coyote optimization algorithm based on adaptive influence weights are proposed, and the shallow neuroevolutionary method based on IGA and the shallow neuroevolutionary method based on ICOA are applied to the weight space of backpropagation neural networks. In deep neuroevolutionary method, the structure space of convolutional neural network is proposed to solve the search space design of neural structure search, and the GA-based deep neuroevolutionary method under the structure space of convolutional neural network is proposed to solve the problem that numerous hyperparameters and network structure parameters can produce explosive combinations when designing deep learning models. The neural network fusion bionic algorithm used has the application value of exploring the spatial structure and dynamics of the socioeconomic system, improving the perception of the socioeconomic situation, and understanding the development law of society, etc. The idea is also verifiable through the present computer technology. (shrink)

The identification and analysis of the spatiotemporal dynamic traffic patterns in citywide road networks constitute a crucial process for complex traffic management and control. However, city-scale and synchronal traffic data pose challenges for such kind of quantification, especially during peak hours. Traditional studies rely on data from road-based detectors or multiple communication systems, which are limited in not only access but also coverage. To avoid these limitations, we introduce real-time, traffic condition digital maps as our input. The digital (...) maps keep spatiotemporal urban traffic information in nature and are open to access. Their pixel colors represent traffic conditions on corresponding road segments. We propose a stacked convolutional autoencoder-based method to extract a low-dimension feature vector for each input. We compute and analyze the distances between vectors. The statistical results show different traffic patterns during given periods. With the actual data of Chongqing city, we compare the feature extraction performance between our proposed method and histogram. The result shows our proposed method can extract spatiotemporal features better. For the same data set, there is little difference in the number distribution of red pixels found in the statistics result of the histogram, while differences do exist in the results of our proposed method. We find the most fluctuated morning is on Friday; the most fluctuated evening is on Tuesday; and the most stable evening is on Wednesday. The distance captured by our method can represent the evolution of different traffic conditions during the morning and evening peak hours. Our proposed method provides managers with assistance to sense the dynamics of citywide traffic conditions in quantity. (shrink)

AimPatients with high myopia reportedly exhibit changes in functional brain activity, but the mechanism underlying such changes is unclear. This study was conducted to observe differences in dynamic spontaneous brain activity between patients with HM and healthy controls via dynamic regional homogeneity analysis.MethodsResting-state functional magnetic resonance imaging scans were performed on 82 patients with HM and 59 HCs who were closely matched for age, sex, and weight. The dReHo approach was used to assess local dynamic activity (...) in the human brain. The association between mean dReHo signal values and clinical symptoms in distinct brain areas in patients with HM was determined via correlation analysis.ResultsIn the left fusiform gyrus, right inferior temporal gyrus, right Rolandic operculum, right postcentral gyrus, and right precentral gyrus, dReHo values were significantly greater in patients with HM than in HCs.ConclusionPatients with HM have distinct functional changes in various brain regions that mainly include the L-FG, R-ITG, R-ROL, R-PoCG, and R-PreCG. These findings constitute important evidence for the roles of brain networks in the pathophysiological mechanisms of HM and may aid in the diagnosis of HM. (shrink)

Dynamics concerns the process of change in variable conditions through time at any level of analysis. Various important issues or topics in stakeholder theory and practice involve consideration of change over time and thus unavoidably involve dynamics. While dynamics has received explicit recognition in stakeholder literature, dynamic analysis remains partly tacit and suffused through the literature. One reason is that dynamics remains difficult to model even in economics. This article provides a basic orientation to stakeholder dynamics as (...) a key conceptual and methodological issue for stakeholder thinking. This article identifies current literature concerning stakeholder dynamics and evaluates future directions in dynamic reasoning that would help build stakeholder theory and improve practice of stakeholder management. Notions of competition, influence strategies, change in stakeholder networks, mindsets, salience or values, learning, creative destruction, long-term sustainability, stakeholder reciprocity, sustainable development, and value creation all embed change and thus time dynamics. Static complexity and heterogeneity across units-of-analysis are not the same as dynamics but do also change over time. Dynamics concerns how change process influences consequences (later in time) in relationship to antecedents (earlier in time). Management copes with change process, as much as with complexity and variation in antecedents and consequences. (shrink)

The problem of finite-time stability of switched genetic regulatory networks with time-varying delays via Wirtinger’s integral inequality is addressed in this study. A novel Lyapunov–Krasovskii functional is proposed to capture the dynamical characteristic of GRNs. Using Wirtinger’s integral inequality, reciprocally convex combination technique and the average dwell time method conditions in the form of linear matrix inequalities are established for finite-time stability of switched GRNs. The applicability of the developed finite-time stability conditions is validated by numerical results.