scholarly journals análisis en redes de aprendizaje organizacional como herramienta en la administración empresarial

Author(s):  
Valentín Inocente Jiménez Jarquín ◽  
Juan Carlos Navarrete Narváez ◽  
Omar García Jimenez ◽  
Francisco Javier Vega Lara

En este trabajo se presentan los avances de nuestra investigación, la cual consiste en la aplicación de la Perspectiva de Redes en el estudio del Proceso de Aprendizaje Organizacional en una empresa de servicio ubicada en la Ciudad de México. El análisis de redes representa una herramienta fundamental en el proceso de modelación de Sistemas, específicamente en este trabajo se trata de sistemas sociales, los cuales se caracterizan como un tipo de Sistemas Complejos. Se propone construir y analizar una red, en la cual los nodos representan a los empleados de la compañía y los arcos simbolizan una relación de aprendizaje colaborativo. Se construye la red y se analiza su estructura utilizando el software PAJEK diseñado por Vladimir & Mrvar (2016). Con el propósito decomprobar tres suposiciones acerca de la misma. La primera suposición es que la estructura de esta red podría aproximarse al modelo de red libre de escala bajo la regla de conexión preferencial, la segunda suposición es que los factores de agregación en la red podrían ser: la experiencia de los empleados, la distancia, el género, relaciones de amistad y jerarquía. AbstractThis paper presents the advances of our research, which consists in the application of the Network Perspective in the study of theOrganizational Learning Process in a service company located in Mexico City. The analysis of networks represents a fundamental tool in the process of modeling of Systems, specifically in this work it deals with social systems, which are characterized as a type of Complex Systems. It is proposed to build and analyze a network, in which the nodes represent the employees of the company and the arcs symbolize a collaborative learning relationship. The network is built and itsstructure is analyzed using the PAJEK software designed by Vladimir & Mrvar (2016). In order to check three assumptions about it. The first assumption is that the structure of this network could approximate the scale-free network model under the preferential connection rule, the second assumption being that aggregation factors in the network could be: employee experience, distance, gender, relations of friendship and hierarchy.KeywordsOrganizational learning, Social network analysis, Complex networks, Organizational learning networks, Complex Systems.

Fractals ◽  
2006 ◽  
Vol 14 (02) ◽  
pp. 101-110
Author(s):  
D. NAGY ◽  
G. TIBÉLY ◽  
J. KERTÉSZ

Hierarchically modular systems show a sequence of scale separations in some functionality or property in addition to their hierarchical topology. Starting from regular, deterministic objects like the Vicsek snowflake or the deterministic scale free network by Ravasz et al., we first characterize the hierarchical modularity by the periodicity of some properties on a logarithmic scale indicating separation of scales. Then we introduce randomness by keeping the scale freeness and other important characteristics of the objects and monitor the changes in the modularity. In the presented examples, a sufficient amount of randomness destroys hierarchical modularity. Our findings suggest that the experimentally observed hierarchical modularity in systems with algebraically decaying clustering coefficients indicates a limited level of randomness.


2009 ◽  
Vol 29 (5) ◽  
pp. 1230-1232
Author(s):  
Hao RAO ◽  
Chun YANG ◽  
Shao-hua TAO

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xiuwen Fu ◽  
Yongsheng Yang ◽  
Haiqing Yao

Previous research of wireless sensor networks (WSNs) invulnerability mainly focuses on the static topology, while ignoring the cascading process of the network caused by the dynamic changes of load. Therefore, given the realistic features of WSNs, in this paper we research the invulnerability of WSNs with respect to cascading failures based on the coupled map lattice (CML). The invulnerability and the cascading process of four types of network topologies (i.e., random network, small-world network, homogenous scale-free network, and heterogeneous scale-free network) under various attack schemes (i.e., random attack, max-degree attack, and max-status attack) are investigated, respectively. The simulation results demonstrate that the rise of interference R and coupling coefficient ε will increase the risks of cascading failures. Cascading threshold values Rc and εc exist, where cascading failures will spread to the entire network when R>Rc or ε>εc. When facing a random attack or max-status attack, the network with higher heterogeneity tends to have a stronger invulnerability towards cascading failures. Conversely, when facing a max-degree attack, the network with higher uniformity tends to have a better performance. Besides that, we have also proved that the spreading speed of cascading failures is inversely proportional to the average path length of the network and the increase of average degree k can improve the network invulnerability.


2018 ◽  
Vol 35 (1) ◽  
pp. 123-132 ◽  
Author(s):  
Lei Zhu ◽  
Lei Wang ◽  
Xiang Zheng ◽  
Yuzhang Xu

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