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Studies of social networks in organizations confront analytical challenges posed by the multilevel effects of hierarchical relations between organizational subunits on the presence or absence of informal network relations among or...
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Studies of social networks in organizations confront analytical challenges posed by the multilevel effects of hierarchical relations between organizational subunits on the presence or absence of informal network relations among organizational members. Conventional multilevel models may be usefully adopted to control for generic forms of non-independence between tie variables defined at multiple levels of analysis. Such models, however, are unable to identify the specific multilevel dependence mechanisms generating the observed network data. This is the basic difference between multilevel analysis of networks and the analysis of multilevel networks. The aim of this article is to show how recently derived multilevel exponential random graph models (MERGMs) may be specified and estimated to address the problems posed by the analysis of multilevel networks in organizations. We illustrate our methodological proposal using data on hierarchical subordination and informal communication relations between top managers in a multiunit industrial group. We discuss the implications of our results in the broader context of current theories of organizations as connected multilevel systems.
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This paper presents the optimal control and synchronization problem of a multilevel network of R?ssler chaotic oscillators. Using the Hamilton-Jacobi-Bellman technique, the optimal control law with a three-state variable feedback ...
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This paper presents the optimal control and synchronization problem of a multilevel network of R?ssler chaotic oscillators. Using the Hamilton-Jacobi-Bellman technique, the optimal control law with a three-state variable feedback is designed such that the trajectories of all the R?ssler oscillators in the network are optimally synchronized at each level. Furthermore, we provide numerical simulations to demon- strate the effectiveness of the proposed approach for the cases of one and three networks. A perfect correlation between the MATLAB and PSpice results was obtained, thus allowing the experimental validation of our designed controller and shows the effectiveness of the theoretical results.
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Management research regularly considers social networks and their effects on a wide range of organizational phenomena. Scholars employing the social network perspective have generated a considerable body of organizational research...
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Management research regularly considers social networks and their effects on a wide range of organizational phenomena. Scholars employing the social network perspective have generated a considerable body of organizational research, with much of this scholarship single-level in its focus: exploring how networks of individuals, groups, or firms relate to organizational outcomes at the same level of analysis. However, given that organizations are multilevel systems, a network theory of the organization should, by definition, be multilevel in its scope, considering how networks at one level of the organizational system influence networks at higher and/or lower levels. In this article, the authors overlay canonical multilevel theory on the social network perspective to derive postulates defining the broad theoretical domain of a multilevel network theory of organization. The link between these two theoretical perspectives is the graph theoretical notion of systems of nested networks, allowing the authors to examine how an observed network structure at one level of the system of organizational networks relates to network structures and effects at higher or lower levels of the system.
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Computing the average shortest-path length (ASPL) of a large scale-free network needs much memory space and computation time. Based on the feature of scale-free network, we present a simplification algorithm by cutting the suspens...
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Computing the average shortest-path length (ASPL) of a large scale-free network needs much memory space and computation time. Based on the feature of scale-free network, we present a simplification algorithm by cutting the suspension points and the connected edges; the ASPL of the original network can be computed through that of the simplified network. We also present a multilevel simplification algorithm to get ASPL of the original network directly from that of the multisimplified network. Our experiment shows that these algorithms require less memory space and time in computing the ASPL of scale-free network, which makes it possible to analyze large networks that were previously impossible due to memory limitations.
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Neural network based image segmentation techniques primarily focus on the selection of appropriate thresholding points in the image feature space. Research initiatives in this direction aim at addressing this problem of effective ...
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Neural network based image segmentation techniques primarily focus on the selection of appropriate thresholding points in the image feature space. Research initiatives in this direction aim at addressing this problem of effective threshold selection for activation functions. Multilevel activation functions resort to fixed and uniform thresholding mechanisms. These functions assume homogeneity of the image information content. In this paper, we propose a collection of adaptive thresholding approaches to multilevel activation functions. The proposed thresholding mechanisms incorporate the image context information in the thresholding process. Applications of these mechanisms are demonstrated on the segmentation of real life multilevel intensity images using a self-supervised multilayer self-organizing neural network (MLSONN) and a supervised pyramidal neural network (PyraNet). We also present a bi-directional self-organizing neural network (BDSONN) architecture suitable for multilevel image segmentation. The architecture uses an embedded adaptive thresholding mechanism to a characteristic multilevel activation function. The segmentation efficiencies of the thresholding mechanisms evaluated using four unsupervised measures of merit, are reported for the three neural network architectures considered.
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Community detection is one of the most popular researches in a variety of complex systems, ranging from biology to sociology. In recent years, there's an increasing focus on the rapid development of more complicated networks, name...
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Community detection is one of the most popular researches in a variety of complex systems, ranging from biology to sociology. In recent years, there's an increasing focus on the rapid development of more complicated networks, namely multilayer networks. Communities in a single-layer network are groups of nodes that are more strongly connected among themselves than the others, while in multilayer networks, a group of well-connected nodes are shared in multiple layers. Most traditional algorithms can rarely perform well on a multilayer network without modifications. Thus, in this paper, we offer overall comparisons of existing works and analyze several representative algorithms, providing a comprehensive understanding of community detection methods in multilayer networks. The comparison results indicate that the promoting of algorithm efficiency and the extending for general multilayer networks are also expected in the forthcoming studies.
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This study explores the evolving structure of the rising field of "network of networks" (NoN). Reviewing publications dating back to 1931, we describe the evolution of major NoN research themes in different scientific disciplines ...
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This study explores the evolving structure of the rising field of "network of networks" (NoN). Reviewing publications dating back to 1931, we describe the evolution of major NoN research themes in different scientific disciplines and the gradual emergence of an integrated field. We analyse the co-occurrence networks of keywords used in all 7818 scientific publications in Scopus database that mention NoN and other related terms (i.e., "interconnected networks", "multilayer networks", "multiplex networks", "interdependent networks", "multinetworks", "multilevel networks", and "multidimensional networks"). The results show that the NoN began to form as a field mainly in the 1990s around research on neural networks. Diverse aspects of NoN research, indicated by dominant keywords such as "interconnection", "multilayer", and "interdependence", gradually spread to computer and physical sciences. As of 2018, network interdependence - with its application in network resilience and prevention of cascading failure - seems to be one of the key topics attracting broad academic attention. Another noteworthy observation is the emergence of a distinct cluster of terms relevant to nanoscience and nanotechnology. It is envisaged from the analysis that NoN concepts will develop stronger ties with nanoscience with increasing understanding and data acquisition from the molecular, atomic, and subatomic levels. (C) 2021 Published by Elsevier B.V.
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Abstract This article seeks to provide theoretical and managerial insights with respect to the following questions: What is the effect of network management on the outcomes of associated firms? Do these effects on outcomes vary am...
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Abstract This article seeks to provide theoretical and managerial insights with respect to the following questions: What is the effect of network management on the outcomes of associated firms? Do these effects on outcomes vary among small-firm networks (SFNs)? Do these outcomes vary among associated firms within the same SFN? Which management elements are most influential in the variation of these SFN outcomes? To answer these questions, this study adopts a multilevel analytical approach using SFNs in southern Brazil that benefit from the Cooperation Networks Program, a local public policy initiative that supports the formation, development, and consolidation of SFNs. The findings suggest that the outcomes provided by the networks differ between networks but are similar for firms in the same network. They also indicate that strategy and processes at the network level are related to firms' outcomes. These findings show that the influence of structure on firms' outcomes varies among networks and that the market segment is the only network-level variable that is significantly related to firms' outcomes.
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Many models for the analysis of spatio-temporal networks specify time as a series of discrete steps. This either requires evenly spaced measurement times or the aggregation of data into measurement windows. This can lead to the in...
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Many models for the analysis of spatio-temporal networks specify time as a series of discrete steps. This either requires evenly spaced measurement times or the aggregation of data into measurement windows. This can lead to the introduction of bias. An alternative is to use continuous-time models, for example, multilevel models. Models capturing complex spatio-temporal variation are often difficult to visualise and interpret. This can be addressed by simplifying the results, for example by extracting 'features' of interest (such as maxima or minima) of temporal patterns associated with different network connections.This paper uses simulation to evaluate the accuracy and precision with which b-spline-based multilevel models (a flexible form of continuous-time model that can easily capture complex variation associated with a spatio-temporal network structure) capture the timing and extent of maximum delays to journeys made between pairs of stations in a small railway network.On average models captured the timing and extent of maximum delay with small bias, but there was evidence of overestimation and underestimation of low and high values of these features, respectively. This systematic bias may have partially caused the undercoverage of credible intervals for the pattern features. Alternative model specifications - specifically to capture x-axis random variation, for example - should be considered in future work.
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Applying a multilevel treatment model to a sample of Spanish manufacturing firms, we evaluate the joint impact of regional and national funding on firms' cooperative relationships. A joint provision of public support from differen...
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Applying a multilevel treatment model to a sample of Spanish manufacturing firms, we evaluate the joint impact of regional and national funding on firms' cooperative relationships. A joint provision of public support from different administrative levels is termed a multilevel innovation policy mix. Because of heterogeneity in innovation behaviour and performance, we separately analyse small and medium-sized enterprises (SMEs) and large firms. Our empirical findings show heterogenous results with respect to both firm size and a type of cooperative partners. For SMEs, a multilevel policy mix has a synergistic effect on cooperation with customers. For other cooperative partners, the joint effectiveness depends on the comparison group. Namely, both sources jointly are more effective than regional support in isolation in promoting cooperation with suppliers and universities. For those SMEs that are funded from central government, a multilevel governance is effective in case of cooperation with government agencies and consultants. With regards to large firms, we find a limited evidence on complementarity between regional and national support. Namely, the policy mix is effective when large firms cooperate with other firms, specifically with customers and competitors. In contrast, empirical findings suggest no complementary effects for cooperation with knowledge providers.
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