Compatibility Measurement in Social Network Analysis: Literature Review
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Published
Nov 30, 2020
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Abstract
Social network has become one of method to discover the position of each agent. Social Network Analysis can visualize the connections nodes who is people in social network. in some purpose, SNA becoming one motivation to understand the detailed characteristics that can be used to enhance the learning environment. Another motivation is to understand the interaction pattern and identity people for real-time. This paper is to compare and find compatibility measurement from across study in SNA. Therefore, the study found that centrality is the most measurement to use in SNA.
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Widiyanti, T. (2020). Compatibility Measurement in Social Network Analysis: Literature Review. Journal of Informatics Information System Software Engineering and Applications (INISTA), 3(1), 45-51. https://doi.org/10.20895/inista.v3i1.164
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[2] G. Erétéo, M. Buffa, F. Gandon, and O. Corby, “Analysis of a real online social network using semantic web frameworks,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5823 LNCS, pp. 180–195, 2009, doi: 10.1007/978-3-642-04930-9_12.
[3] B. Saxena and V. Saxena, “Hurst exponent based approach for influence maximization in social networks,” J. King Saud Univ. - Comput. Inf. Sci., no. xxxx, 2020, doi: 10.1016/j.jksuci.2019.12.010.
[4] W. Maharani and A. A. Gozali, “Collaborative Social Network Analysis and Content-based Approach to Improve the Marketing Strategy of SMEs in Indonesia,” Procedia Comput. Sci., vol. 59, no. Iccsci, pp. 373–381, 2015, doi: 10.1016/j.procs.2015.07.540.
[5] F. N. Koranteng, I. Wiafe, F. A. Katsriku, and R. Apau, “Understanding trust on social networking sites among tertiary students: An empirical study in Ghana,” Appl. Comput. Informatics, no. xxxx, 2019, doi: 10.1016/j.aci.2019.07.003.
[6] L. Pedro, C. Santos, J. Batista, G. Cabral, F. Pais, and C. Costa, “Social Network Analysis and Digital Learning Environments: a Framework for Research and Practice Using the Sapo Campus Platform,” INTED2016 Proc., vol. 1, no. April, pp. 1061–1070, 2016, doi: 10.21125/inted.2016.1239.
[7] A. A. Made Kevin Bratawisnu, “Social network analysis untuk analisa interaksi user dimedia sosial mengenai bisnis e-commerce,” vol. 2, no. 2, pp. 107–115, 2018.
[8] S. R. K, R. KVSVN, and V. V. Kumari, “Application of Clustering to Analyze Academic Social Networks,” Int. J. Web Semant. Technol., vol. 4, no. 2, pp. 9–20, 2013, doi: 10.5121/ijwest.2013.4202.
[9] S. Umeda, M. Nakano, H. Mizuyama, H. Hibino, D. Kiritsis, and G. von Cieminski, “Advances in production management systems: Innovative production management towards sustainable growth: IFIP WG 5.7 international conference, APMS 2015 Tokyo, Japan, september 7-9, 2015 proceedings, Part I,” IFIP Adv. Inf. Commun. Technol., vol. 459, pp. 36–44, 2015, doi: 10.1007/978-3-319-22756-6.
[10] B. Davey and A. Tatnall, “in Education - An Actor-Network Analysis Three Computer Systems in Victorian School Communities,” pp. 160–169, 2013.
[11] N. Emami, N. Mozafari, and A. Hamzeh, “Continuous state online influence maximization in social network,” Soc. Netw. Anal. Min., vol. 8, no. 1, pp. 1–17, 2018, doi: 10.1007/s13278-018-0510-5.
[12] F. Ghafoor and M. A. Niazi, “Using social network analysis of human aspects for online social network software: a design methodology,” Complex Adapt. Syst. Model., vol. 4, no. 1, 2016, doi: 10.1186/s40294-016-0024-9.
[13] P. Gloor, M. Paasivaara, D. Schoder, and P. Willems, “Correlating performance with social network structure through teaching social network analysis,” IFIP Int. Fed. Inf. Process., vol. 224, pp. 265–272, 2006, doi: 10.1007/978-0-387-38269-2_28.
[14] M. R. Padmanabhan, N. Somisetty, S. Basu, and A. Pavan, “Influence Maximization in Social Networks with Non-Target Constraints,” Proc. - 2018 IEEE Int. Conf. Big Data, Big Data 2018, pp. 771–780, 2019, doi: 10.1109/BigData.2018.8621973.
[15] A. Marcus and N. Krishnamurthi, “Cross-cultural analysis of social network services in Japan, Korea, and the USA,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5623 LNCS, pp. 59–68, 2009, doi: 10.1007/978-3-642-02767-3_7.
[16] O. E. Llantos and M. R. J. E. Estuar, “Characterizing instructional leader interactions in a social learning management system using social network analysis,” Procedia Comput. Sci., vol. 160, no. 2018, pp. 149–156, 2019, doi: 10.1016/j.procs.2019.09.455.
[17] L. Ni, Y. Yuan, X. Wang, J. Yu, and J. Zhang, “A Privacy Preserving Algorithm Based on R-constrained Dummy Trajectory in Mobile Social Network,” Procedia Comput. Sci., vol. 129, pp. 420–425, 2018, doi: 10.1016/j.procs.2018.03.018.
[18] H. Schwartz-Chassidim, O. Ayalon, T. Mendel, R. Hirschprung, and E. Toch, “Selectivity in posting on social networks: the role of privacy concerns, social capital, and technical literacy,” Heliyon, vol. 6, no. 2, p. e03298, 2020, doi: 10.1016/j.heliyon.2020.e03298.
[19] S. A. Semenkovich and O. A. Tsukanova, “On the Algorithms of Identifying Opinion Leaders in Social Networks,” Procedia Comput. Sci., vol. 162, no. Itqm 2019, pp. 778–785, 2019, doi: 10.1016/j.procs.2019.12.050.
[20] R. J. Maxwell and R. Filgueira, “Key players in the Grieg NL Placentia Bay Atlantic Salmon Aquaculture Project: A social network analysis,” Mar. Policy, vol. 113, p. 103800, 2020, doi: 10.1016/j.marpol.2019.103800.
[21] J. Martinez-Romo, G. Robles, J. M. Gonzalez-Barahona, and M. Ortuño-perez, “Using social network analysis techniques to study collaboration between a FLOSS community and a company,” IFIP Int. Fed. Inf. Process., vol. 275, pp. 171–186, 2008, doi: 10.1007/978-0-387-09684-1_14.
[22] D. R. Recupero, S. Consoli, A. Gangemi, A. G. Nuzzolese, and D. Spampinato, “The Semantic Web: ESWC 2014 Satellite Events,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8798, pp. 245–248, 2014, doi: 10.1007/978-3-319-11955-7.