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Relevant bibliographies by topics / Self-organizing map

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Academic literature on the topic 'Self-organizing map'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Self-organizing map"

1

Rougier, Nicolas P., and Georgios Is Detorakis. "Randomized Self-Organizing Map." Neural Computation 33, no. 8 (2021): 2241–73. http://dx.doi.org/10.1162/neco_a_01406.

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We propose a variation of the self-organizing map algorithm by considering the random placement of neurons on a two-dimensional manifold, following a blue noise distribution from which various topologies can be derived. These topologies possess random (but controllable) discontinuities that allow for a more flexible self-organization, especially with high-dimensional data. The proposed algorithm is tested on one-, two- and three-dimensional tasks, as well as on the MNIST handwritten digits data set and validated using spectral analysis and topological data analysis tools. We also demonstrate t

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Tatoian, Robert, and Lutz Hamel. "Self-Organizing Map Convergence." International Journal of Service Science, Management, Engineering, and Technology 9, no. 2 (2018): 61–84. http://dx.doi.org/10.4018/ijssmet.2018040103.

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Self-organizing maps are artificial neural networks designed for unsupervised machine learning. Here in this article, the authors introduce a new quality measure called the convergence index. The convergence index is a linear combination of map embedding accuracy and estimated topographic accuracy and since it reports a single statistically meaningful number it is perhaps more intuitive to use than other quality measures. The convergence index in the context of clustering problems was proposed by Ultsch as part of his fundamental clustering problem suite as well as real world datasets. First d

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Kohonen, T. "The self-organizing map." Proceedings of the IEEE 78, no. 9 (1990): 1464–80. http://dx.doi.org/10.1109/5.58325.

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Vuorimaa, Petri. "Fuzzy self-organizing map." Fuzzy Sets and Systems 66, no. 2 (1994): 223–31. http://dx.doi.org/10.1016/0165-0114(94)90312-3.

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Kohonen, Teuvo. "The self-organizing map." Neurocomputing 21, no. 1-3 (1998): 1–6. http://dx.doi.org/10.1016/s0925-2312(98)00030-7.

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Pal, Sankar K., Biswarup Dasgupta, and Pabitra Mitra. "Rough Self Organizing Map." Applied Intelligence 21, no. 3 (2004): 289–99. http://dx.doi.org/10.1023/b:apin.0000043561.99513.69.

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Saini, Naveen, Sriparna Saha, Sahil Mansoori, and Pushpak Bhattacharyya. "Fusion of self-organizing map and granular self-organizing map for microblog summarization." Soft Computing 24, no. 24 (2020): 18699–711. http://dx.doi.org/10.1007/s00500-020-05104-2.

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Sampath, R., and A. Saradha. "Alzheimer’s Disease Image Segmentation with Self-Organizing Map Network." Journal of Software 10, no. 6 (2015): 670–80. http://dx.doi.org/10.17706//jsw.10.6.67-680.

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Sampath, R., and A. Saradha. "Alzheimer’s Disease Image Segmentation with Self-Organizing Map Network." Journal of Software 10, no. 6 (2015): 670–78. http://dx.doi.org/10.17706//jsw.10.6.670-680.

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FURUKAWA, Tetsuo. "Self-organizing map of a set of self-organizing maps." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 19, no. 6 (2007): 618–26. http://dx.doi.org/10.3156/jsoft.19.6_618.

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Dissertations / Theses on the topic "Self-organizing map"

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Farshad, Tabrizi Seyed Ramin. "The Probabilistic Supervised Self-Organizing Map, PSSOM." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ31828.pdf.

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Choe, Yoonsuck. "Perceptual grouping in a self-organizing map of spiking neurons." Access restricted to users with UT Austin EID Full text (PDF) from UMI/Dissertation Abstracts International, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3025202.

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Schwardt, Martin. "Lösung ausgewählter Routenplanungsprobleme mit Hilfe der self-organizing map." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=975255126.

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Pourkia, Javid. "A SELF-ORGANIZING MAP APPROACH FOR HOSPITAL DATA ANALYSIS." OpenSIUC, 2014. https://opensiuc.lib.siu.edu/theses/1553.

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In this work, we utilize Self Organized Maps (SOM) to cluster and classify hospital related data with large dimensions, provided by Medicare website. These data have published every year and it includes numerous measures for each hospital in the nationwide. It might be possible to unearth some correlations in health-care industry by being able to interpreting this dataset, for example by examining the relations between data of immunizations department to readmission records and hospital expenses. It is not feasible to make any sense from these measures altogether using traditional methods (2D

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Žáček, Viktor. "Kohonenova samoorganizační mapa." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219527.

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Work deal about self-organizing maps, especially about Kohonen self-organizing map. About creating of aplication, which realize creating and learning of self-organizing map. And about usage of self-organizing map for self-localization of robot.

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Malondkar, Ameya Mohan. "Extending the Growing Hierarchical Self Organizing Maps for a Large Mixed-Attribute Dataset Using Spark MapReduce." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33385.

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In this thesis work, we propose a Map-Reduce variant of the Growing Hierarchical Self Organizing Map (GHSOM) called MR-GHSOM, which is capable of handling mixed attribute datasets of massive size. The Self Organizing Map (SOM) has proved to be a useful unsupervised data analysis algorithm. It projects a high dimensional data onto a lower dimensional grid of neurons. However, the SOM has some limitations owing to its static structure and the incapability to mirror the hierarchical relations in the data. The GHSOM overcomes these shortcomings of the SOM by providing a dynamic structure that adap

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Wang, Dali. "Adaptive Double Self-Organizing Map for Clustering Gene Expression Data." Fogler Library, University of Maine, 2003. http://www.library.umaine.edu/theses/pdf/WangD2003.pdf.

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Mewes, Daniel, and Ch Jacobi. "Analyzing Arctic surface temperatures with Self Organizing-Maps: Influence of the maps size." Universität Leipzig, 2018. https://ul.qucosa.de/id/qucosa%3A31794.

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We use ERA-Interim reanalysis data of 2 meter temperature to perform a pattern analysis of the Arctic temperatures exploiting an artificial neural network called Self Organizing-Map (SOM). The SOM method is used as a cluster analysis tool where the number of clusters has to be specified by the user. The different sized SOMs are analyzed in terms of how the size changes the representation of specific features. The results confirm that the larger the SOM is chosen the larger will be the root mean square error (RMSE) for the given SOM, which is followed by the fact that a larger number of pattern

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Bui, Michael. "Path finding on a spherical self-organizing map using distance transformations." Thesis, The University of Sydney, 2008. http://hdl.handle.net/2123/9290.

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Spatialization methods create visualizations that allow users to analyze high-dimensional data in an intuitive manner and facilitates the extraction of meaningful information. Just as geographic maps are simpli ed representations of geographic spaces, these visualizations are esssentially maps of abstract data spaces that are created through dimensionality reduction. While we are familiar with geographic maps for path planning/ nding applications, research into using maps of high-dimensional spaces for such purposes has been largely ignored. However, literature has shown that it is possible t

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Tervonen, J. (Jaakko). "Exploring behaviour patterns with self-organizing map for personalised mental stress detection." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201904131491.

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Abstract. Stress is an important health problem and the cause for many illnesses and working days lost. It is often measured with different questionnaires that capture only the current stress levels and may come in too late for early prevention. They are also prone to subjective inaccuracies since the feeling of stress, and the physiological response to it, have been found to be individual. Real-time stress detectors, trained on biosignals like heart rate variability, exist but majority of them employ supervised learning which requires collecting a large amount of labelled data from each syste

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Books on the topic "Self-organizing map"

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Klaus, Obermayer, and Sejnowski Terrence J, eds. Self-organizing map formation: Foundations of neural computation. MIT Press, 2001.

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Sahai, A. K. An objective study of Indian summer monsoon variability using the self organizing map algorithms. Indian Institute of Tropical Meteorology, 2006.

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United States. National Aeronautics and Space Administration., ed. Control of the NASA Langley 16-foot transonic tunnel with the self-organizing feature map. National Aeronautics and Space Administration, 1998.

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United States. National Aeronautics and Space Administration., ed. Control of the NASA Langley 16-foot transonic tunnel with the self-organizing feature map. National Aeronautics and Space Administration, 1998.

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United States. National Aeronautics and Space Administration., ed. Control of the NASA Langley 16-foot transonic tunnel with the self-organizing feature map. National Aeronautics and Space Administration, 1998.

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United States. National Aeronautics and Space Administration., ed. CONTROL OF THE NASA LANGLY 16-FOOT TRANSONIC TUNNEL WITH THE SELF-ORGANIZING FEATURE MAP... NASA/TM-98-206722... FEB. 25, 1998. s.n., 1999.

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Kohonen, Teuvo. Self-organizing maps. Springer, 1995.

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Kohonen, Teuvo. Self-organizing maps. 2nd ed. Springer, 1997.

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Tokutaka, Heizō. Jiko soshikika mappu to sono ōyō. Maruzen Kabushiki Kaisha, 2012.

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J, Deboeck Guido, and Kohonen Teuvo, eds. Visual explorations in finance: With self-organizing maps. Springer, 1998.

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Book chapters on the topic "Self-organizing map"

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Lucchini, Mario, and Davide Bussi. "Self-Organizing Map." In Encyclopedia of Quality of Life and Well-Being Research. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17299-1_104673.

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Lucchini, Mario, and Davide Bussi. "Self-Organizing Map." In Encyclopedia of Quality of Life and Well-Being Research. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-69909-7_104673-1.

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Grigore, Ovidiu. "Syntactical Self-Organizing Map." In Computational Intelligence Theory and Applications. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62868-1_103.

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Attik, Mohammed, Laurent Bougrain, and Frédéric Alexandre. "Self-organizing Map Initialization." In Artificial Neural Networks: Biological Inspirations – ICANN 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550822_56.

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Shekhar, Shashi, and Hui Xiong. "Self Organizing Map Usage." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1182.

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Neuland, Michaela, Mehdi Amirijoo, and Thomas Kürner. "Appendix B: X-Map Estimation for LTE." In Self-Organizing Networks. John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119954224.app2.

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Honkela, Timo, Jorma Laaksonen, Hannele Törrö, and Juhani Tenhunen. "Media Map: A Multilingual Document Map with a Design Interface." In Advances in Self-Organizing Maps. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21566-7_25.

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Yu, Dongjun, Jun Hu, Xiaoning Song, Yong Qi, and Zhenmin Tang. "Supervised Kernel Self-Organizing Map." In Intelligent Science and Intelligent Data Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36669-7_31.

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Sarlin, Peter. "Self-Organizing Financial Stability Map." In Computational Risk Management. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54956-4_7.

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Yu, Dongjun, Edwin R. Hancock, and William A. P. Smith. "A Riemannian Self-Organizing Map." In Image Analysis and Processing – ICIAP 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_26.

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Conference papers on the topic "Self-organizing map"

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Salgado, Paulo, Teresa Perdicoullis, P. Lopes dos Santos, and Paulo A. F. N. A. Afonso. "Hierarchical Self-Organizing Map as Nonlinear Classificator." In 2024 IEEE 24th International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2024. https://doi.org/10.1109/cinti63048.2024.10830843.

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Dozono, Hiroshi, Gen Niina, and Satoru Araki. "Convolutional Self Organizing Map." In 2016 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2016. http://dx.doi.org/10.1109/csci.2016.0149.

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Chow, Chi Kin, and Shiu Yin Yuen. "Signal Self Organizing Map." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4370957.

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Wu, Yingxin, and Masahiro Takatsuka. "Geodesic self-organizing map." In Electronic Imaging 2005, edited by Robert F. Erbacher, Jonathan C. Roberts, Matti T. Grohn, and Katy Borner. SPIE, 2005. http://dx.doi.org/10.1117/12.586807.

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Dozono, Hiroshi, Ryuhei Matsuo, and Koki Yoshioka. "Reservoir Self Organizing Map." In 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS). IEEE, 2022. http://dx.doi.org/10.1109/scisisis55246.2022.10001926.

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"ASSOCIATIVE SELF-ORGANIZING MAP." In International Conference on Neural Computation. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0002318403630370.

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Kumar, D. Indra, and Manjunath R. Kounte. "Comparative study of self-organizing map and deep self-organizing map using MATLAB." In 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2016. http://dx.doi.org/10.1109/iccsp.2016.7754303.

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Shang, Qing-Zhen, and Hong-Jie Xing. "Correntropy based self-organizing map." In 2016 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2016. http://dx.doi.org/10.1109/icmlc.2016.7860929.

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Yeloglu, Ozge, A. Nur Zincir-Heywood, and Malcolm I. Heywood. "Growing recurrent self organizing map." In 2007 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icsmc.2007.4414001.

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Ayu, Aqila Dzikra, Hansel Matthew, Iman Herlambang Suherman, Aries Subiantoro, and Benyamin Kusumoputro. "Development of Autonomous Control System using Self-Organizing Map and Autoregressive Self-Organizing Map." In 2022 9th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). IEEE, 2022. http://dx.doi.org/10.23919/eecsi56542.2022.9946634.

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Reports on the topic "Self-organizing map"

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Eguchi, Junji, and Manabu Murakami. Application of Self-Organizing Map to Inspection Technology for Gear Surface. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0575.

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Herrera, Allen, and Alexander Heifetz. Detection of Anomalies in Gamma Background Radiation Data with K-Means and Self-Organizing Map Clustering Algorithms - Consortium on Nuclear Security Technologies (CONNECT) Q1 Report. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1841591.

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Ortiz, M. Growing Self-Organizing Maps as Predictors for Photometric Redshift. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1557954.

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Ponmalai, R., and C. Kamath. Self-Organizing Maps and Their Applications to Data Analysis. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1566795.

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Soroko, Nataliia V., Lorena A. Mykhailenko, Olena G. Rokoman, and Vladimir I. Zaselskiy. Educational electronic platforms for STEAM-oriented learning environment at general education school. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3884.

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The article is devoted to the problem of the use of educational electronic platform for the organization of a STEAM-oriented environment of the general school. The purpose of the article is to analyze the use of educational electronic platforms for organizing the STEAM-oriented school learning environment and to identify the basic requirements for supporting the implementation and development of STEAM education in Ukraine. One of the main trends of education modernization is the STEAM education, which involves the integration between the natural sciences, the technological sciences, engineerin

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Khairulin, Alexander, Vladimir Blinkov, Lyubov Lagunova, et al. Electronic training course "Theoretical foundations of physical culture". SIB-Expertise, 2024. http://dx.doi.org/10.12731/er0788.29012024.

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The electronic training course “Theoretical Foundations of Physical Culture” contains fifteen topics that allow students to understand: the basic concepts of physical culture (PC) and sports; its role in general cultural and professional training; socio-biological foundations of PC; the role of motor activity for modern man; the basics of a healthy lifestyle; means of PC in the regulation of life activity; basics of general physical and sports training; the basics of the theory and methodology of physical education; the basics of methods for organizing and conducting independent physical educa

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Martinez, Hector, Kylie Rochford, and Diane Bergeron. Helping You, Helping Me? The Mediating Role of Organizational Citizenship Behavior in the Relationship Between Psychological Capital and Social Network Positions. Center for Creative Leadership, 2025. https://doi.org/10.35613/ccl.2025.2061.

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Over the past few decades, there has been an increasing shift from focusing on what is wrong with humans and organizations (e.g., problems, maladaptive behavior) to focusing on what is right (e.g., strengths, human and organizational flourishing). In this positive vein, we investigated the explanatory role of organizational citizenship behavior (e.g., helping others, volunteering for tasks that help the group or organization, organizing events, speaking up with ideas) in examining the relationship between psychological capital and social network positions. Psychological capital is an individua

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