scholarly journals Toward a more Efficient Knowledge Network in Innovation Ecosystems: A Simulated Study on Knowledge Management

2020 ◽  
Vol 12 (16) ◽  
pp. 6328
Author(s):  
Houxing Tang ◽  
Zhenzhong Ma ◽  
Jiuling Xiao ◽  
Lei Xiao
Keyword(s):  
Knowledge Management ◽  
Random Network ◽  
Knowledge Exchange ◽  
Network Models ◽  
Small World ◽  
Knowledge Diffusion ◽  
Knowledge Network ◽  
Innovation Ecosystem ◽  
Exchange Networks ◽  
Regular Network

Knowledge management has become increasingly important in the era of knowledge economy. This study explores what is an optimal knowledge network for more efficient knowledge diffusion among strategic partners in order to provide insights on sustainable enterprises and a more knowledge-efficient innovation ecosystem. Based on simulated analyses of the efficiency of knowledge network models, including regular network, random network, and small world network, this study shows that a random knowledge network is more efficient for knowledge diffusion when a mixture knowledge trade rule is used. This study thus helps identify which knowledge networks facilitate knowledge exchange among collaborative partners for sustainable knowledge management. Management practitioners and policymakers can use the findings to design more appropriate knowledge exchange networks to improve the efficiency of knowledge diffusion in an innovation ecosystem.

scholarly journals Factor or Network Model? Predictions From Neural Networks

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Alexander P. Christensen ◽  
Keyword(s):  
Neural Networks ◽  
Network Model ◽  
Latent Variable ◽  
Random Network ◽  
Network Models ◽  
Small World ◽  
Small World Network ◽  
Monte Carlo Simulation Study ◽  
Psychological Phenomena ◽  
The Neural Networks

The nature of associations between variables is important for constructing theory about psychological phenomena. In the last decade, this topic has received renewed interest with the introduction of psychometric network models. In psychology, network models are often contrasted with latent variable (e.g., factor) models. Recent research has shown that differences between the two tend to be more substantive than statistical. One recently developed algorithm called the Loadings Comparison Test (LCT) was developed to predict whether data were generated from a factor or small-world network model. A significant limitation of the current LCT implementation is that it's based on heuristics that were derived from descriptive statistics. In the present study, we used artificial neural networks to replace these heuristics and develop a more robust and generalizable algorithm. We performed a Monte Carlo simulation study that compared neural networks to the original LCT algorithm as well as logistic regression models that were trained on the same data. We found that the neural networks performed as well as or better than both methods for predicting whether data were generated from a factor, small-world network, or random network model. Although the neural networks were trained on small-world networks, we show that they can reliably predict the data-generating model of random networks, demonstrating generalizability beyond the trained data. We echo the call for more formal theories about the relations between variables and discuss the role of the LCT in this process.

MICROSCOPIC SELF-ORGANIZATION IN NETWORKS AND REGULAR LATTICE

2002 ◽  
Vol 16 (25) ◽  
pp. 923-935
Author(s):  
QI OUYANG ◽  
KAI SUN ◽  
HONGLI WANG
Keyword(s):  
Correlation Length ◽  
Random Network ◽  
Reaction System ◽  
Small World ◽  
Self Organization ◽  
Fast Diffusion ◽  
Small World Network ◽  
Regular Lattice ◽  
Numerical Studies ◽  
Regular Network

We report our numerical studies on the microscopic self-organizations of a reaction system in three types of networks: a regular network, a small-world network, and a random network as well as on a regular lattice. Our simulation results show that the topology of the network has an important effect on the communication among reaction molecules, and plays an important role in microscopic self-organization. The correlation length among reacting molecules in a random or a small-world network is much shorter compared with that in the regular network. As a result, it is much easier to obtain a microscopic self-organization in a small-world or a random network. A phase transition from a stochastic state to a synchronized state was observed when the randomness of a small-world network was increased. We also demonstrate that good synchronization activities of enzymatic turnover cycles can be developed on a regular lattice when the correlation length created by the fast diffusion of regulatory particles is large enough.

STOCHASTIC RESONANCE IN HINDMARSH–ROSE NEURAL NETWORK WITH SMALL-WORLD CONNECTIONS

2008 ◽  
Vol 22 (30) ◽  
pp. 5365-5373 ◽  
Author(s):  
RENHUAN YANG ◽  
AIGUO SONG
Keyword(s):  
Neural Network ◽  
Stochastic Resonance ◽  
Random Network ◽  
Small World ◽  
Resonance Curve ◽  
Resonance Phenomenon ◽  
Node Degree ◽  
Regular Network ◽  
Stochastic Resonance Phenomenon ◽  
Peak Value

We study stochastic resonance (SR) in Hindmarsh–Rose (HR) neural network with small-world (SW) connections driven by external periodic stimulus, focusing on the dependence of properties of SR on the network structure parameters. It is found that, the SW neural network enhances SR compared with single neuron. By turning coupling strength c, two categories of SR were gained. With the connection-rewiring probability p increasing, the resonance curve becomes more and more sharp and the peak value increases gradually and then reaches saturation. The SW network enhances the SR peak value compared with regular network and widens resonance in ascending range compared with random network. When decreasing node degree k, the resonance range is enlarged, and the signal noise ratio (SNR) curve becomes a two peak one from a classic single peak SR curve, and then the stochastic resonance phenomenon almost disappears.

Knowledge Exchange Protocols: A Second Study

2003 ◽  
Vol 02 (02) ◽  
pp. 153-163 ◽  
Author(s):  
Richard Herschel ◽  
Hamid Nemati ◽  
David Steiger
Keyword(s):  
Knowledge Management ◽  
Tacit Knowledge ◽  
Explicit Knowledge ◽  
Knowledge Exchange ◽  
Conversion Process ◽  
Knowledge Network ◽  
Knowledge Conversion ◽  
Management Domain ◽  
Amplification Process ◽  
Use Of Knowledge

In the knowledge management domain, the conversion of tacit knowledge to explicit knowledge is critical because it is a prerequisite to the knowledge amplification process wherein knowledge becomes part of an organization's knowledge network. Moreover, this process is strategically important because it enhances an organization's ability to create new knowledge that is inevitably expressed through the organization's capabilities, products, and services. The conversion of tacit to explicit knowledge is particularly relevant to information technology (IT), because IT can only partially facilitate tacit knowledge management, while it offers a substantial number of techniques to support the management and sharing of explicit knowledge. In this paper, knowledge exchange protocols are examined as a vehicle for improving the tacit-to-explicit knowledge conversion process. In a second experiment testing the use of knowledge exchange protocols, initial findings are confirmed: while structure may significantly improve the tacit-to-explicit knowledge conversion process, it also matters how the structure is employed in this process.

scholarly journals How Does the Heterogeneity of Members Affect the Evolution of Group Opinions?

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
An Lu ◽  
Haifeng Ling ◽  
Zhengping Ding
Keyword(s):  
Decision Making ◽  
Random Network ◽  
Small World ◽  
Important Influence ◽  
The Other ◽  
Network Structures ◽  
Small World Network ◽  
Average Amount ◽  
Regular Network ◽  
The Impact

Understanding the impact of heterogeneity on the evolution of group opinions can enlighten us on how to effectively organize, redesign, and improve decision-making efficiency. This article explores mainly the effects of heterogeneity on the evolution of group opinions. It is found that the heterogeneity of individuals’ openness has an important influence on the ability to aggregate group opinions. According to the average amount of clusters and Herfindahl–Hirschman Index (HHI) under different network structures, heterogeneity often improves the ability. In addition, for the small-world network and random network, there is little difference in the aggregation ability from both the average amount of clusters and the Herfindahl–Hirschman Index. While for the regular network, the ability is obviously weaker than that of the other two. This result also shows that the randomness of interaction between members will enhance the cohesion of a group.

Knowledge Management under Coopetition

2011 ◽  
pp. 791-803
Keyword(s):  
Knowledge Management ◽  
Knowledge Transfer ◽  
Knowledge Exchange ◽  
Organizational Knowledge ◽  
Exchange Networks ◽  
Knowledge Management Initiatives

This article deals with Knowledge Management under Coopetition and, in this context, illustrates the concept of Coopetitive Learning and Knowledge Exchange Networks (CoLKENs). It investigates the setting for inter-organizational knowledge management initiatives focusing on issues related to cooperation-competition-dilemmas and intentional/unintentional knowledge transfer.

A New Small-World Neural Network with its Performance on Fault Tolerance

2012 ◽  
Vol 629 ◽  
pp. 719-724
Author(s):  
Xiao Hu Li ◽  
Feng Xu ◽  
Jin Hua Zhang ◽  
Su Nan Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Tolerance ◽  
Random Network ◽  
Small World ◽  
Simple Simulation ◽  
Neuron Damage ◽  
Regular Network ◽  
Artificial Neural ◽  
And Function

Many artificial neural networks are the simple simulation of brain neural network’s architecture and function. However, how to rebuild new artificial neural network which architecture is similar to biological neural networks is worth studying. In this study, a new multilayer feedforward small-world neural network is presented using the results form research on complex network. Firstly, a new multilayer feedforward small-world neural network which relies on the rewiring probability heavily is built up on the basis of the construction ideology of Watts-Strogatz networks model and community structure. Secondly, fault tolerance is employed in investigating the performances of new small-world neural network. When the network with connection fault or neuron damage is used to test the fault tolerance performance under different rewiring probability, simulation results show that the fault tolerance capability of small-world neural network outmatches that of the same scale regular network when the fault probability is more than 40%, while random network has the best fault tolerance capability.

Application of Lattice Imaging Techniques to Amorphous Films

1975 ◽  
Vol 33 ◽  
pp. 8-9
Author(s):  
S. R. Herd ◽  
P. Chaudhari
Keyword(s):  
Nearest Neighbor ◽  
Random Network ◽  
Imaging Techniques ◽  
Network Models ◽  
Accurate Method ◽  
Direct Transmission ◽  
Lattice Imaging ◽  
Transmission Electron ◽  
Lattice Planes ◽  
Nearest Neighbor Distances

Electron diffraction and direct transmission have been used extensively to study the local atomic arrangement in amorphous solids and in particular Ge. Nearest neighbor distances had been calculated from E.D. profiles and the results have been interpreted in terms of the microcrystalline or the random network models. Direct transmission electron microscopy appears the most direct and accurate method to resolve this issue since the spacial resolution of the better instruments are of the order of 3Å. In particular the tilted beam interference method is used regularly to show fringes corresponding to 1.5 to 3Å lattice planes in crystals as resolution tests.

scholarly journals Sublinear domination and core–periphery networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marios Papachristou
Keyword(s):  
Network Model ◽  
Power Law ◽  
Real World ◽  
Random Network ◽  
Small World ◽  
The Core ◽  
Entire Network

AbstractIn this paper we devise a generative random network model with core–periphery properties whose core nodes act as sublinear dominators, that is, if the network has n nodes, the core has size o(n) and dominates the entire network. We show that instances generated by this model exhibit power law degree distributions, and incorporates small-world phenomena. We also fit our model in a variety of real-world networks.

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