Dynamic Network Process Research Articles

O desempenho das regiões econômicas do Tocantins em tempos de crise (2010-2015)

O principal objetivo deste artigo consistiu em analisar a relação entre interações espaciais e crises nas Regiões Econômicas do Tocantins (TO). Por dependerem dos recursos oriundos da União e também pelos serviços ofertados nos polos nacionais, Brasília e São Paulo, qualquer perturbação advinda desses lugares afeta negativamente as suas bases produtivas. Mensurou-se esse fenômeno por meio do I de Moran Global e Local (LISA) em dois períodos (2010/2012 e 2013/2015). Os resultados apontaram que as relações espaciais entre as Regiões Econômicas do Tocantins foram impactadas pelas crises de diferentes formas, sendo que no norte do estado houve uma maior retração de suas atividades produtivas. Na faixa central, onde localiza a capital, Palmas, ocorreu recuperação em vista da aproximação entre as principais centralidades urbanas do estado. Nesse sentido, afirmou-se que esse cenário aconteceu diante das diferenças de conhecimentos produtivos entre as regiões do Tocantins. Sendo assim, ressaltou-se a importância de se analisar as aglomerações urbanas como forma de superar crises. 
 Palavras-chave: Interações Espaciais. Economia Regional. Crises

Controlling seizure propagation in large-scale brain networks.

Information transmission in the human brain is a fundamentally dynamic network process. In partial epilepsy, this process is perturbed and highly synchronous seizures originate in a local network, the so-called epileptogenic zone (EZ), before recruiting other close or distant brain regions. We studied patient-specific brain network models of 15 drug-resistant epilepsy patients with implanted stereotactic electroencephalography (SEEG) electrodes. Each personalized brain model was derived from structural data of magnetic resonance imaging (MRI) and diffusion tensor weighted imaging (DTI), comprising 88 nodes equipped with region specific neural mass models capable of demonstrating a range of epileptiform discharges. Each patient’s virtual brain was further personalized through the integration of the clinically hypothesized EZ. Subsequent simulations and connectivity modulations were performed and uncovered a finite repertoire of seizure propagation patterns. Across patients, we found that (i) patient-specific network connectivity is predictive for the subsequent seizure propagation pattern; (ii) seizure propagation is characterized by a systematic sequence of brain states; (iii) propagation can be controlled by an optimal intervention on the connectivity matrix; (iv) the degree of invasiveness can be significantly reduced via the proposed seizure control as compared to traditional resective surgery. To stop seizures, neurosurgeons typically resect the EZ completely. We showed that stability analysis of the network dynamics, employing structural and dynamical information, estimates reliably the spatiotemporal properties of seizure propagation. This suggests novel less invasive paradigms of surgical interventions to treat and manage partial epilepsy.

Constructing urban dynamic transportation planning strategies for improving quality of life and urban sustainability under emerging growth management principles

Since the beginning of twenty-first century, urban development worldwide has successively deviated from our ideal design and original plans. There have subsequently accompanied numerous urban problems, such as congestion, serious air pollution, urban endlessly sprawl and inappropriate land development with low urban density. All of these features we often called urban illnesses significantly created negative influence to our urban life. The growth management concepts were thus emerged firstly to confront such negative influence. Not uniquely, numerous developed countries also failed to achieve its initial development strategy goals and soon afterwards implemented a more specific and essential strategies in which it introduced three innovative growth constructions: smart growth, sustainable growth, and inclusive growth. They confidently claimed that urban development would certainly improve quality of life (QoL) if the concept of development may be replaced by the concept of growth. Following various innovative growth constructions or management principles, contemporary urban agents including practical planners or official decision units, thus could pursue urban sustainable development and QoL simultaneously.Based on the current status and development processes, this study aimed at the urban transportation planning strategy and its dynamics according to the advanced international experiences. Firstly, this study surveyed international experiences or researches related to quality of life concepts, growth management principles as well as sustainable transport issues. Secondly, we then make an effort to identify critical sustainable transportation indicators under various growth constructions or management principles through the Fuzzy Delphi Method (FDM) modeling results. The FDM requires repetitive surveys to confirm the final convergent values of all expert opinions and exclude less feasible sustainable transportation indicators at the same time. Thirdly, the Dynamic Network Process (DNP) was also applied to the expert survey to obtain four different weights during the period from 2014 to 2040 in response to actual changes or future trends of each indicator we selected. And then this research combined Gray Forecasting Model (GFM) to reflect changes or trends of each indicator over time by above several DNP surveys for making more realistic and suitable priorities or weights to achieve the efficiency of resource allocation. Finally, this study tried to conduct an empirical analysis with New Taipei City in Taiwan to indicate our planning philosophy and consequently validate planning strategies emphasized by this study. We believes that research frameworks and findings here not only provide new insights into the functioning of current growth constructions or management principles, but also could be used as a well reference basis for practical planners or official decision units to facilitate QoL and urban sustainability in further.

Investigating the regional sustainable performance of the Chinese real estate industry: A slack-based DEA approach

This paper aims to investigate the regional sustainable performance of real estate industry of 30 sample provinces on China's mainland from 2007 to 2013, based on a slack-based data envelopment analysis (DEA) approach. We assume that the real estate industry operates with a dynamic three-stage network process. The proposed model specification is dynamic because carryover variables produced in a previous period that constrain the current production are considered. The empirical results show the major findings: (a) the growing speeds of Assets, Completed investment on land and Completed investment in houses are far faster than that of gross domestic product (GDP) in the same period, which indicates the possibility of too much existing investment; (b) the inefficiency status of the Chinese real estate industry increased after 2012; (c) there is severe imbalance among different provinces; (d) there is a need to improve the operational efficiencies of real estate industries in some provinces, especially Qinghai, Gansu, Ningxia, Xinjiang, Hainan, Guizhou, and Shanxi; and (e) GDP and Price of commercial real estate both have negative effects on slack-based inefficiency, on average, in the examined period. (f) The Herfindahl-Hirschman index and other variables can be used to explain the level and difference in inefficiency bias over time and across regions. Based on those findings, we propose several policy suggestions for sustainable development of Chinese real estate industry.

Multiobjective discrete particle swarm optimization for community detection in dynamic networks

Tracking and identifying the dynamic patterns of evolving communities has recently drawn great attention. How to detect the community structure in a dynamic network has become a popular problem in the field of complex network and evolutionary computing. As a new concept, evolutionary clustering, is proposed to detect the process of dynamic networks under the temporal smoothness framework. Evolutionary-based clustering approaches try to maximize clustering accuracy at the current time step and minimize clustering drift at two successive time steps. But the low accuracy and the pre-setting of parameters limit their effectiveness. In order to overcome these weaknesses, in this paper, the community detection in a dynamic network is transformed into a multiobjective optimization problem. Specifically, we propose a novel decomposition strategy for multiobjective discrete particle swarm optimizationm, which balances the accuracy and the smoothness. The experimental results on synthetic and real-world datasets demonstrate the superiority of the proposed method compared with other state-of-the-art methods.

A new knowledge-based link recommendation approach using a non-parametric multilayer model of dynamic complex networks

Traditionally, research on network theory focused on studying graphs with equivalent entities failing to deliberate the useful supplementary information related to the dynamic properties of the complex network interactions. This paper tries to study the evolution process of dynamic complex networks from a multilayer perspective by analyzing the properties of naturally multilayered web-based directed complex social networks of Google+ and Twitter, and undirected collaborative networks of DBLP and ASTRO-PH, thereby proposing a new non-parametric knowledge-based multilayer link recommendation approach. The paper investigates the layers’ evolution throughout the network evolution, inspects the evolution of each node's membership in different layers by an Infinite Factorial Hidden Markov Model, and finally formulates the intra-layer and inter-layer link generation process. Some Markov Chain Monte Carlo sampling strategies are driven to simulate parameters of the proposed multilayer model, using certain synthetic and real complex network datasets. Experimental results indicate great improvements in the performance of the proposed multilayer link recommendation approach in terms of certain analyzed performance measures.

The network dynamics of co-offending careers

Despite the long-standing acknowledgement that crime is a group phenomenon, little research treats co-offending as a dynamic network process. This study analyses the individual and network processes responsible for long-lasting criminal relationships using co-offending dyads from eight years of arrest records in Chicago. Results from proportional hazard models suggest that homophily with respect to age, race, gender, geographic proximity, and gang identity lead to sustained partnerships. Victimization increases the probability of continued co-offending, while the victimization of one’s associates dissuade continued collaboration. Supra-dyadic processes (centrality, transitivity) influence the likelihood of continued co-offending. Results are discussed regarding opportunities and turning points.

NATERGM: A Model for Examining the Role of Nodal Attributes in Dynamic Social Media Networks

Social media networks are dynamic. As such, the order in which network ties develop is an important aspect of the network dynamics. This study proposes a novel dynamic network model, the Nodal Attribute-based Temporal Exponential Random Graph Model (NATERGM) for dynamic network analysis. The proposed model focuses on how the nodal attributes of a network affect the order in which the network ties develop. Temporal patterns in social media networks are modeled based on the nodal attributes of individuals and the time information of network ties. Using social media data collected from a knowledge sharing community, empirical tests were conducted to evaluate the performance of the NATERGM on identifying the temporal patterns and predicting the characteristics of the future networks. Results showed that the NATERGM demonstrated an enhanced pattern testing capability and an increased prediction accuracy of network characteristics compared to benchmark models. The proposed NATERGM model helps explain the roles of nodal attributes in the formation process of dynamic networks.

Evolutionary Process of Social Capital Formation through Community Currency Organizations: The Japanese Case

Community currency systems are said to influence the revival of communities by promoting either local economic growth or social capital accumulation. However, no empirical studies have examined the multiple competing mechanisms for providing social support through transactional networks among participants. The current study connects network structural concepts to theories of social capital, transaction costs, homophily, and resource dependency at multiple levels and evaluates transactional relationships for community rebuilding and local economic development. We examine the evolutionary process of dynamic networks among local residents or organization members with network configurations in one of the largest Japanese community currency systems, “Peanuts.” Using longitudinal network data over 12 years for approximately 1400 actors, we conclude that the evolution and achievement of transactional network dynamics and partner selections differ between the two groups of participants: individual members and organization members. We also provide practical implications for sustaining participants’ transactions and commitment.

Strategy-aligned fuzzy approach for market segment evaluation and selection: a modular decision support system by dynamic network process (DNP)

Abstract In competitive markets, market segmentation is a critical point of business, and it can be used as a generic strategy. In each segment, strategies lead companies to their targets; thus, segment selection and the application of the appropriate strategies over time are very important to achieve successful business. This paper aims to model a strategy-aligned fuzzy approach to market segment evaluation and selection. A modular decision support system (DSS) is developed to select an optimum segment with its appropriate strategies. The suggested DSS has two main modules. The first one is SPACE matrix which indicates the risk of each segment. Also, it determines the long-term strategies. The second module finds the most preferred segment-strategies over time. Dynamic network process is applied to prioritize segment-strategies according to five competitive force factors. There is vagueness in pairwise comparisons, and this vagueness has been modeled using fuzzy concepts. To clarify, an example is illustrated by a case study in Iran's coffee market. The results show that success possibility of segments could be different, and choosing the best ones could help companies to be sure in developing their business. Moreover, changing the priority of strategies over time indicates the importance of long-term planning. This fact has been supported by a case study on strategic priority difference in short- and long-term consideration.

Large-scale Systems Control and Complex Networks——Exploration and Thinking

With the rapid development of communication and network technology, more and more complex dynamic networks appear in the fields of social, economic and human s daily life. Network science as a new interdisciplinary subject has gained great achievements in the research on performance characteristic, evolution process and control method of complex dynamic networks. The control theory and methodology of large scale systems, aiming at the behavior analysis,control and optimization for high dimensional dynamic systems, should be able to provide a helpful reference for the study of complex networks. In this paper, against some hotspot problems in the research of complex dynamic networks, we explore the possibility of using the control theory and methodology of large scale systems to solve the structural analysis and control problems of complex networks, analyze the facing difculties and the possible coping ways. For control and optimization of large scale complex dynamic networks, a multi-layer hierarchical structure is proposed which integrates the macroscope analysis methods of network science, the quantitative design methods of control science and the intelligent processing methods of information science.

Network dynamics of television use in school classes

Seventy years ago Paul Lazarsfeld and colleagues found empirical evidence for a significant influence of the social context on media effects by interpersonal communication. Subsequently various theories in mass media science incorporated the social context as an independent or dependent variable. However, there is little empirical research that addresses the dynamics of media use within a social context using network analytic methods. This study considers the social context as both an independent and dependent variable in a dynamic network process in order to disentangle social selection and influence processes. Hence, on one hand it tests whether the intensity of TV use and the use of specific TV genres predict the selection of conversation partners in social groups (social selection process). On the other hand it tests if individuals’ social context predicts their TV use (social influence process). Here, social context is defined as the conversation structure as well as the media use of all other persons in the social group.The research design includes a four wave panel survey on interpersonal communication networks and TV use of 707 students (age 13–16) in 29 Swiss school classes. The stochastic actor-based models tested with the program SIENA support the hypothesized selection processes. Conversation partners about TV programs are selected according to the similarity of their TV use. In contrast to this finding, the widely held assumption that individuals are influenced by their social network is not supported. Some inconclusive evidences suggest a possible social influence process on the level of TV intensity but not on the level of specific TV genres. Network-autocorrelation of conversation ties and TV use has therefore primarily be accredited to social selection processes and not social influence. Furthermore, the results show that avid viewers tend to talk more often about TV programs (ego effect), that avid viewers are more likely to be addressed (alter effect), and a general tendency to talk to persons with the same program preference (similarity effect). This challenges the classic idea of a two-step flow of communication in which intensive media users which are well informed would provide information to occasional users which are less conversant with a topic.

USING AN ANALYTIC NETWORK PROCESS MODEL IN COMBINATORIAL AUCTIONS

Auctions are important market mechanisms for the allocation of goods and services. Auctions are preferred often to other common processes because they are open, quite fair, easiness to understand by participants, and lead to economically efficient outcomes. Design of auctions is a multidisciplinary effort made of contributions from economics, operations research, informatics, and other disciplines. Combinatorial auctions are those auctions in which bidders can place bids on combinations of items, called packages, rather than just individual items. The advantage of combinatorial auctions is that the bidder can more fully express his preferences. This is particular important when items are complements. The multiple evaluation criteria can be used. There are dependencies among sellers, buyers, criteria, bundles of items. A variety of feedback processes creates complex system of items. For the whole structure seems to be very appropriate Analytic Network Process (ANP) approach. The ANP method makes possible to deal systematically with all kinds of dependence and feedback in the system of items. By the ANP approach can be evaluated the preferences of bundles of items. Dynamic Network Process (DNP) as an extension of ANP can deal with time dependent priorities in combinatorial auctions. http://dx.doi.org/10.13033/ijahp.v1i2.47

Dynamic business network process management in instant virtual enterprises

Nowadays, business supply chains for the production of complex products or services are likely to involve a number of autonomous organizations. The competitive market requires that these supply chains are highly agile, effective and efficient. Agility and effectiveness are obtained by forming highly dynamic virtual enterprises within supplier networks. We call these instant virtual enterprises (IVEs). The required efficiency of creating and operating IVEs can only be obtained by automated support for design, setup and enactment of business processes within these IVEs. This process support involves the dynamic composition of local processes of network members into global processes at the IVE level. This functionality goes significantly beyond traditional approaches for interorganizational workflow management. The approach, architecture and technology required for this dynamic network process management in IVEs are outlined in this paper. We show how the developed approach is applied in the automotive industry in the context of the CrossWork IST project.

An ANP/DNP analysis of economic elements in today’s world network economy

The network economy is a term for today’s global relationship among economic elements characterized by massive connectivity. The central act of the new era is to connect everything to everything in deep web networks at many levels of mutually interdependent relations, where resources and activities are shared, markets are enlarged and costs and risk are reduced. Network systems contain both positive and negative feedback. A variety of feedback processes create complex system behavior. For such a network the Analytic Network Process (ANP) approach seems to be very appropriate. The ANP method makes it possible to deal systematically with all kinds of dependence and feedback in the system. Dynamic models try to reflect changes in real or simulated time and take into account that the network model components are constantly evolving. Dynamic models use concepts of state variables, flows, and feedback processes. The Dynamic Network Process (DNP) is an extension of ANP that can deal with time dependent priorities in a networked economy.

Force-Based Incremental Algorithm for Mining Community Structure in Dynamic Network

Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104. Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman's algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.

Investigating the dynamic behavior of biochemical networks using model families

Supporting the evolutionary modeling process of dynamic biochemical networks based on sampled in vivo data requires more than just simulation. In the course of the modeling process, the modeler is typically concerned not only with a single model but also with sequences, alternatives and structural variants of models. Powerful automatic methods are then required to assist the modeler in the organization and the evaluation of alternative models. Moreover, the structure and peculiarities of the data require dedicated tool support. To support all stages of an evolutionary modeling process, a new general formalism for the combinatorial specification of large model families is introduced. It allows for automatic navigation in the space of models and excludes biologically meaningless models on the basis of elementary flux mode analysis. An incremental usage of the measured data is supported by using splined data instead of state variables. With MMT2, a versatile tool has been developed as a computational engine intended to be built into a tool chain. Using automatic code generation, automatic differentiation for sensitivity analysis and grid computing technology, a high performance computing environment is achieved. MMT2 supplies XML model specification and several software interfaces. The performance of MMT2 is illustrated by several examples from ongoing research projects. http://www.simtec.mb.uni-siegen.de/ wiechert@simtec.mb.uni-siegen.de.