Statistical inference for continuous-time Markov processes with block structure based on discrete-time network data

A widely used approach to modeling discrete-time network data assumes that discrete-time network data are generated by an unobserved continuous-time Markov process. While such models can capture a wide range of network phenomena and are popular in social network analysis, the models are based on the homogeneity assumption that all nodes share the same parameters. We remove the homogeneity assumption by allowing nodes to belong to unobserved subsets of nodes, called blocks, and assuming that nodes in the same block have the same parameters, whereas nodes in distinct blocks have distinct parameters. The resulting models capture unobserved heterogeneity across nodes and admit model-based clustering of nodes based on network properties chosen by researchers. We develop Bayesian data-augmentation methods and apply them to discrete-time observations of an ownership network of non-financial companies in Slovenia in its critical transition from a socialist economy to a market economy. We detect a small subset of shadow-financial companies that outpaces others in terms of the rate of change and the desire to accumulate stocks of other companies.     

CordNet: Toward a distributed behavior model for emergency response coordination

Existing studies of coordination theory in human networks have looked at coordination problems requiring stable working relationships with no environmental uncertainties. With emergency response management demanding distributed coordination in volatile situations, the designs of existing models are useful as a building block, yet flawed for application. We hypothesize that changes to interconnectedness of nodes in the network may have implications on the potential to coordinate. To test our hypotheses, we investigate survey data from state law enforcement, state emergency services, and local law enforcement by performing agency‐based (macro) and cross‐agency (micro) analysis to identify attributes of each network and coordination.     

异因同果关联神经网络的研究

提出了一种异因同果关联神经网络模型,可以从不同角度分别建立不同的模型,并由其得到互不相同的模型预测值。异因同果关联神经网络模型将不同角度建立的模型有机结合起来,进而能够将多个神经网络模型进行综合考虑,得到一个综合的统一的模型预测结果。研究了新型模型的机理,结合实例进行仿真并与传统的神经网络模型的预测仿真结果比较,结果表明新型模型具有更高的预测精度。     

From network ties to network structures: Exponential Random Graph Models of interorganizational relations

Theoretical accounts of network ties between organizations emphasize the interdependence of individual intentions, opportunities, and actions embedded in local configurations of network ties. These accounts are at odds with empirical models based on assumptions of independence between network ties. As a result, the relation between models for network ties and the observed network structure of interorganizational fields is problematic. Using original fieldwork and data that we have collected on collaborative network ties within a regional community of hospital organizations we estimate newly developed specifications of Exponential Random Graph Models (ERGM) that help to narrow the gap between theories and empirical models of interorganizational networks. After controlling for the main factors known to affect partner selection decisions, full models in which local dependencies between network ties are appropriately specified outperform restricted models in which such dependencies are left unspecified and only controlled for statistically. We use computational methods to show that networks based on empirical estimates produced by models accounting for local network dependencies reproduce with accuracy salient features of the global network structure that was actually observed. We show that models based on assumptions of independence between network ties do not. The results of the study suggest that mechanisms behind the formation of network ties between organizations are local, but their specification and identification depends on an accurate characterization of network structure. We discuss the implications of this view for current research on interorganizational networks, communities, and fields.     

Structural comparisons of networks and model-based detection of small-worldness

In this paper, we consider the problem of assessing the “level of small-worldness” of a graph and of detecting small-worldness features in real networks. After discussing the limitations of classical approaches, based on the computation of network indicators, we propose a new procedure, which involves the comparison of network structures at different “observation scales”. This allows small-world features to be caught, even if “hidden” deeply into the network structure. Applications of the procedure to both simulated and real data show the effectiveness of the proposal, also in distinguishing between different small-world models and in detecting emerging small-worldness in dynamical networks.     

Modelling and Forecasting Gas Flow on Exits of Gas Transmission Networks

The flow of natural gas within a gas transmission network is studied with the aim to optimize such networks. The analysis of real data provides a deeper insight into the behaviour of gas in‐ and outflow. Several models for describing dependence between the maximal daily gas flow and the temperature on network exits are proposed. A modified sigmoidal regression is chosen from the class of parametric models. As an alternative, a semi‐parametric regression model based on penalized splines is considered. The comparison of models and the forecast of gas loads for very low temperatures based on both approaches is included. The application of the obtained results is discussed.     

Models of representation of social mobility and inequality systems. A neural network approach

This paper reports on the results of the application of an innovative technique, i.e. neural network models, to mobility data. Our primary aim is to show that the technique is more flexible than traditional statistical modeling, and that it entails less strong methodological assumptions concerning the phenomenon which they are intended to represent. Two kinds of networks have been applied: heteroassociative networks, used for prevision and class membership recognition; and autoassociative networks, used for simulation tasks. Results obtained from experiments with neural networks on Italian data are highly consistent with the body of knowledge derived from previous classical analysis. The explicative power of neural network models proved to be higher than that of path analysis given their capacity to uncover any kind or relation between variables, whether linear or nonlinear. When compared to log-linear models, they enable the reconstruction of mobility processes within a global frame, controlling all relevant variables at once.     

A network approach for assembling and linking input–output models

Input–output (IO) models, describing trade between different sectors and regions, are widely used to study the environmental repercussions of human activities. A frequent challenge in assembling an IO model or linking several such models is the absence of flow data with the same level of detail for all components. Such problems can be addressed using proportional allocation, which is a form of algebraic transformations. In this paper, we propose a novel approach whereby the IO system is viewed as a network, the topology of which is transformed with the addition of virtual nodes so that available empirical flow data can be mapped directly to existing links, with no additional estimation required, and no impact on results. As IO systems become increasingly disaggregated, and coupled to adjacent databases and models, the adaptability of IO frameworks becomes increasingly important. We show that topological transformations also offer large advantages in terms of transparency, modularity and increasingly importantly for global IO models, efficiency. We illustrate the results in the context of trade linking, multi-scale integration and other applications.     

提高无线传感器网络的运行寿命方法

针对无线传感器网络的节点一般体积小,携带能量低,运算能力弱,能量一旦耗尽不可再生这一特点,本文提出提高网络运行寿命的方法。一方面,在网络节点运行时间上,将基站作为主控中心,根据系统的需求,采用有效的调度算法,来唤醒需要传输的无线传感器节点进行数据采集,然后将传输信息发送给簇头进行网络内传输,直至到达基站,从而获得传感器节点最小运行时间。另一方面,在网络内多跳通信时,靠近基站的簇头节点由于转发大量数据导致节点过早失效问题,本文提出一种不均匀分簇多跳算法,根据节点与基站距离调整簇首概率,能量优先为原则选择簇首,使据基站近的区域簇头多于据基站远的区域。     

Crisis Management Competencies

Since public networks became widespread, doubts have arisen over how to make them succeed. Scholars have traditionally addressed the issue in different ways, thus variously shedding light on the network structure, mechanisms, or managers as predictors of the network performance. The aim of our article is to explore the possibility of an interaction effect between the abovementioned factors. Our results show that there may be a relationship between network structure, mechanisms, and managers that jointly affects network performance. Therefore, important suggestions can be made about how to manage public networks successfully: (1) ensure that your network mechanisms and managerial abilities are coherent with the structure of your network; and (2) if you are in a well-established and integrated network, allow yourself some flexibility. Data were collected through a multiple case study that focused on collaboration for joint provision of home care services in Switzerland.     

Tempus volat,hora fugit: A survey of tie-oriented dynamic network models in discrete and continuous time

Given the growing number of available tools for modeling dynamic networks, the choice of a suitable model becomes central. The goal of this survey is to provide an overview of tie-oriented dynamic network models. The survey is focused on introducing binary network models with their corresponding assumptions, advantages, and shortfalls. The models are divided according to generating processes, operating in discrete and continuous time. First, we introduce the temporal exponential random graph model (TERGM) and the separable TERGM (STERGM), both being time-discrete models. These models are then contrasted with continuous process models, focusing on the relational event model (REM). We additionally show how the REM can handle time-clustered observations, that is, continuous-time data observed at discrete time points. Besides the discussion of theoretical properties and fitting procedures, we specifically focus on the application of the models on two networks that represent international arms transfers and email exchange, respectively. The data allow to demonstrate the applicability and interpretation of the network models.     

Leaders emergence in artificial populations: the role of networks

Social networks can be viewed as graphs in their basic form, but many networks of interest include positive and negative affect relations among nodes. The structural balance theory originally developed by Heider suggests how nodes may locally modify their relationships to maintain a sort of balance within sets of nodes. We analyze a model of leadership emergence in a social network and extend it by introducing structural balance among members when modeling the attitude toward the leader. This approach takes into account some of the mutual relationships among co-workers, including the adaptation process to achieve the balance. This component helps to explore differently the bounded rationality of agents when interacting, and prove the difficulty of finding a rapid and smooth covergent path to a social stable equilibrium.     

Estimating individual action dispositions using binary and frequency egocentric sexual network data

The degree distribution of nodes in a sexual network has been under thorough investigation, as has its implications for the spread of sexually transmitted infections. However, not only the structure of the network is of importance in regulating the propagation of an infection. Two nodes connected by an edge may take actions that reduce the transmission probability through that edge. Condom use is one such action. In this article, we derive models for individual action dispositions, and how they together generate an outcome on the edge connecting two nodes. We derive two main models: One where two connected nodes generate one outcome together (suitable for casual sex partners), and one where they generate several outcomes together (suitable for steady sex partners). We model different disposition distributions and different rules on how the dispositions generate outcomes, using an egocentric network dataset on condom use behavior.     

The influence of supply network structure on firm innovation

In this study, we examine the structural characteristics of supply networks and investigate the relationship between a firm's supply network accessibility and interconnectedness and its innovation output. We also examine potential moderating effects of absorptive capacity and supply network partner innovativeness on innovation output. We hypothesize that firms will experience greater innovation output from (1) higher levels of supply network accessibility and supply network interconnectedness, (2) the interaction between the levels of these two structural characteristics, (3) the moderating role of absorptive capacity on supply network accessibility and the moderating role of supply network partner innovativeness on supply network interconnectedness. Supply network partner relationships are drawn in the context of the electronics industry using data from multiple sources. We use social network analysis to create measures for each supply network structural characteristic. Using regression techniques to test the relationship between these structural characteristics and firm innovation for a sample of 390 firms, our findings suggest that supply network accessibility has a significant association with a firm's innovation output. The results also indicate that interconnected supply networks strengthen the association between supply network accessibility and innovation output. Moreover, the influence of the two structural characteristics on innovation output can be enhanced by a firm's absorptive capacity and level of supply network partner innovativeness. By addressing the need for deeper structural analysis, this study contributes to supply chain research by accounting for the embedded nature of ties in supply networks, and showing how these structural characteristics influence the knowledge and information flows residing within a firm's supply network.     

Forecasting exchange rates using feedforward and recurrent neural networks

In this paper we investigate the out-of-sample forecasting ability of feedforward and recurrent neural networks based on empirical foreign exchange rate data. A two-step procedure is proposed to construct suitable networks, in which networks are selected based on the predictive stochastic complexity (PSC) criterion, and the selected networks are estimated using both recursive Newton algorithms and the method of nonlinear least squares. Our results show that PSC is a sensible criterion for selecting networks and for certain exchange rate series, some selected network models have significant market timing ability and/or significantly lower out-of-sample mean squared prediction error relative to the random walk model.     

Comparative evaluation of community-aware centrality measures

Influential nodes play a critical role in boosting or curbing spreading phenomena in complex networks. Numerous centrality measures have been proposed for identifying and ranking the nodes according to their importance. Classical centrality measures rely on various local or global properties of the nodes. They do not take into account the network community structure. Recently, a growing number of researches have shifted to community-aware centrality measures. Indeed, it is a ubiquitous feature in a vast majority of real-world networks. In the literature, the focus is on designing community-aware centrality measures. However, up to now, there is no systematic evaluation of their effectiveness. This study fills this gap. It allows answering which community-aware centrality measure should be used in practical situations. We investigate seven influential community-aware centrality measures in an epidemic spreading process scenario using the Susceptible–Infected–Recovered model on a set of fifteen real-world networks. Results show that generally, the correlation between community-aware centrality measures is low. Furthermore, in a multiple-spreader problem, when resources are available, targeting distant hubs using Modularity Vitality is more effective. However, with limited resources, diffusion expands better through bridges, especially in networks with a medium or strong community structure.

     

Artificial stock markets with different maturity levels: simulation of information asymmetry and herd behavior using agent-based and network models

This paper aims mainly at building artificial stock markets with different maturity levels by modeling information asymmetry and herd behavior. The developed artificial markets are multi-assets, order-driven and populated by agents having heterogeneous behaviors and information. Agents are defined by their information and their herd behavior levels. Agents trade multiple risky assets based on their wealth, their behaviors and their available information which spread among multiple behavioral networks. In a novel contribution to artificial stock markets literature, agents’ behaviors modeling is mixed with social network simulation to reproduce different degrees of information asymmetry and herd behavior based on several assortative topologies. Several simulations validated the proposed model since univariate and multivariate stylized facts were reproduced both for mature and immature stock markets. The proposed artificial stock market can be considered as a first step toward decision and simulation tools for optimal management, strategy analysis and predictions evolution of immature stock markets.     

Models for data transmission delay in cable networks

Cable networks have been upgraded in recent years to support bidirectional traffic rather than just unidirectional television signals. Thus, it has become possible to use cable networks for internet browsing and telephony. In this paper, we propose delay models to determine how this bidirectional traffic is best handled and to evaluate the resulting performance of the network, notably its delay properties. It is shown that these models, variations on the repairman problem and the bulk service queue, lead to actual improvements in the data transmission schedules for cable networks. Moreover, these models can be combined in order to arrive at approximations for the average packet delay. Our theoretical calculations are backed up by simulations.     

GMM estimation of social interaction models with centrality

This paper considers the specification and estimation of social interaction models with network structures and the presence of endogenous, contextual, correlated, and group fixed effects. When the network structure in a group is captured by a graph in which the degrees of nodes are not all equal, the different positions of group members as measured by the Bonacich (1987) centrality provide additional information for identification and estimation. In this case, the Bonacich centrality measure for each group can be used as an instrument for the endogenous social effect, but the number of such instruments grows with the number of groups. We consider the 2SLS and GMM estimation for the model. The proposed estimators are asymptotically efficient, respectively, within the class of IV estimators and the class of GMM estimators based on linear and quadratic moments, when the sample size grows fast enough relative to the number of instruments.     

Trade networks and economic fluctuations in Asian countries

The paper presents a new methodology, based on tensor decomposition, to map dynamic trade networks and to assess its strength in forecasting economic fluctuations at different periods of time in Asia. Using the monthly merchandise import and export data across 33 Asian economies, together with the US, EU and UK, we detect the community structure of the evolving network and we identify clusters and central nodes inside each of them. Our findings show that data are well represented by two communities, in which People's Republic of China and Japan play the major role. We then analyze the synchronisation between GDP growth and trade. Furthermore we apply our model to the prediction of economic fluctuations. Our findings show that the model leads to an increase in predictive accuracy, as higher order interactions between countries are taken into account.