Abstract
Analysis of social networks is an example of dynamically developing research tool that finds its practical application in studying several aspects of organizational activity and helps to better understand it. It focuses on analysis of the structure and connections between different elements of the organization, represented by nodes (referring, for example, to people) and edges (often representing different relationships between these people). Social network analysis offers a lot of measures which allow one to analyze different properties of the network, for example, determining the positions of selected persons in a given organization or discovering different groups of interests. The paper studies the organizational social network based on e-mail communication and focuses on detection of communities in this network. It presents the results of the computational experiment, which allowed one to compare the detected communities with real structure of the organization. The study confirmed the power of the social network analysis where communities have been detected correctly by the best algorithm when compare to the organizational structure
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References
Albert, R., Jeong, H., Barabasi, A.-L.: Diameter of the World-Wide Web. Nature 401, 130–131 (1999)
Bedi, P., Sharma, Ch.: Community detection in social networks. WIREs Data Mining Knowl. Discov. 6, 115–135 (2016)
Blondel, D.V., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Experiment 10, 10008 (2008)
Boccaletti, S., Ivanchenko, M., Latora, V., Pluchino, A., Rapisarda, A.: Detecting complex network modularity by dynamical clustering. Phys. Rev. E 75(4) (2007)
Christidis, P., Losada, A.G.: Email based institutional network analysis: applications and risks. Soc. Sci. 8(306) (2019)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70 (2004)
Coscia, M., Giannotti, F., Pedreschi, D.: A classification for community discovery methods in complex networks. Stat. Anal. Data Min. 4(5), 512–546 (2011)
Creamer, G., Rowe, R., Hershkop, S., Stolfo, S.J.: Segmentation and automated social hierarchy detection through email network analysis. In: Zhang, H., et al. (eds.) WebKDD/SNA-KDD 2007. LNCS, vol. 5439, pp. 40–58. Springer, Berlin, Heidelberg (2009)
Donath, W., Hoffman, A.: Lower bounds for the partitioning of graphs. IBM J. Res. Dev. 17(5), 420–425 (1973)
Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99, 7821–7826 (2002)
Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning. Springer, Berlin (2001)
Kolli, N., Narayanaswamy, B.: Analysis of e-mail communication using a social network framework for crisis detection in an organization. Proc.-Soc. Behav. Sci. 100, 57–67 (2013)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Cam, L.M.L., Neyman, J. (eds.) Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)
Merten, F., Gloor, P.: Too much e-mail decreases job satisfaction. Proc. Soc. Behav. Sci. 2, 6457–65 (2010)
Michalski, R., Palus, S., Kazienko, P.: Matching organizational structure and social network extracted from email communication. In: Abramowicz, W. (eds.) Business Information Systems. BIS 2011. LNBIP, vol. 87, pp. 197–206. Springer, Berlin, Heidelberg (2011)
Newman, M.E.J.: The structure of scientific collaboration networks. PNAS 98(2), 404–409 (2001)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), (2004)
Pastor-Satorras, R., Vespignani, A.: Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86, 3200–3203 (2001)
Pons, P., Latapy, M.: Computing communities in large networks using random walks. J. Graph Algor. Appl. 10(2), 191–218 (2006)
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. PNAS 101(9) (2004)
Ravasz, E., Somera, A., Mongru, D.A., Oltvai, Z.N., Barabasi, A.-L.: Hierarchical organization of modularity in metabolic networks. Science 297(5586), 1551–1555 (2002)
Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Phys. Rev. E 74(1) (2006)
Szyman, P.: Community identification in social network based on e-mail communication using modularity. [in Polish: Identyfikacja spolecznosci w sieci spolecznosciowej opartej na komunikacji e-mail przy użyciu modularnosci.] In: Antonowicz, P., Beben, R., Ploska, R. (eds.) Spoleczne i niematerialne determinanty rozwoju przedsiebiorstw, Wydawnictwo Uniwersytetu Gdanskiego, Gdansk, pp. 47–62 (2019)
Csárdi, G.: https://cran.r-project.org/web/packages/igraph/igraph.pdf. Accessed 04 Feb 2021
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Barbucha, D., Szyman, P. (2021). Detecting Communities in Organizational Social Network Based on E-mail Communication. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2765-1_2
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