Social Network Analysis. Группа авторов
Чтение книги онлайн.

Читать онлайн книгу Social Network Analysis - Группа авторов страница 11

Название: Social Network Analysis

Автор: Группа авторов

Издательство: John Wiley & Sons Limited

Жанр: Техническая литература

Серия:

isbn: 9781119836735

isbn:

СКАЧАТЬ training data, without which the performance of the classifier cannot be analyzed. One of the commonly used statistical classifier is the Naïve Bayes classifier, which is generally used to classify the sentiments of people in COVID pandemic conditions. Such kind of classifiers generally utilizes the publicly available data (from the communal media data) in an efficient way to perform a prediction or analysis or classification problems.

Schematic illustration of flowchart of social network.

      There are a number of metrics available for the SN analysis methods that measure the activity of the social users/nodes and ensure a better understanding of the analysis [32, 33]. Some of the metrics are discussed as follows:

      1.5.1 Centrality

      1.5.2 Transitivity and Reciprocity

      The linking characteristics of a network can be accessed using the transitivity and reciprocity metrics. The transitive nature between three edges can be analyzed using the transitivity metric in such a way to develop a triangle, and in the same way, the transitive nature of a node is analyzed using the reciprocity metrics.

      1.5.3 Balance and Status

      The consistency of the networks can be evaluated using the social balance and social status metrics. The social balance theory states that a friend relationship is consistent with the propagation of the transitivity among nodes as “the friend of my friend is my friend.” Hence, the consistent triangles, depending on this strategy, are represented as balanced.

      SN organization examination is the way toward researching social designs using organizations and chart hypothesis. It consolidates the assortment of strategies for examining the construction of interpersonal organizations just as speculations that target clarifying the hidden elements; furthermore, designs are seen in these constructions. It is an intrinsically interdisciplinary field, which initially rose up out of the fields of social brain research, insights, and chart hypothesis.

      1. Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B., Measurement and analysis of online social networks, in: Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, pp. 29–42, 2007.

      2. Scott, J. and Carrington, P.J., The SAGE handbook of social network analysis. London: SAGE publications ltd, 2014.

      3. Holme, P. and Saramäki, J., Temporal networks, Physics reports, vol. 519, pp. 97–125, 2012.

      4. Lee, S., Rocha, L.E., Liljeros, F., Holme, P., Exploiting temporal network structures of human interaction to effectively immunize populations. PloS One, 7, 5, e36439, 2012.

      5. Pennacchioli, D., Rossetti, G., Pappalardo, L., Pedreschi, D., Giannotti, F., Coscia, M., The three dimensions of social prominence, in: Proceedings of International Conference on Social Informatics, pp. 319–332, 2013.

      6. Rossetti, G., Guidotti, R., Miliou, I., Pedreschi, D., Giannotti, F., A supervised approach for intra-/inter-community interaction prediction in dynamic social networks. Soc Netw. Anal. Min., 6, 1, 1–20, 2016.

      7. Camacho, D., Panizo-LLedot, Á., Bello-Orgaz, G., Gonzalez-Pardo, A., Cambria, E., The four dimensions of social network analysis: An overview of research methods, applications, and software tools. Inform. Fusion, 63, 88–120, 2020.

      8. Akhtar, N., Social network analysis tools, in: proceedings of Fourth International Conference on Communication Systems and Network Technologies, pp. 388–392, 2014.

      9. Mohr, I., The impact of social media on the fashion industry. JABE, 15, 2, 17–22, 2013.

      10. Nash, J., Exploring how social media platforms influence fashion consumer decisions in the UK retail sector, J. Fash. Mark. Manage, 23, 1, 82–103, 2019. https://doi.org/10.1108/JFMM-01-2018-0012

      11. Yu, Y. Moore, M. and Parillo-Chapman, L., Social media based, data-mining driven Social Network Analysis (SNA) of Printing Technologies in Fashion Industry, International Textile and Apparel Association Annual Conference Proceedings, 77, 1, 2020. https://doi.org/10.31274/itaa.11762

      12. Kate, S., Wickremasinghe, D., Blanchet, K., Avan, B., Schellenberg, J., Use of social network analysis methods to study professional advice and performance among healthcare providers: a systematic review. Syst. Rev., 6, 1, 1–23, 2017.

      13. Wang, P., González, M.C., Menezes, R., Barabási, A.L., Understanding the spread of malicious mobile-phone programs and their damage potential. Int. J. Inf. Secur., 12, 5, 383–392, 2013.

      14. Burt, R.S., Social contagion and innovation: Cohesion versus structural equivalence. Am. J. Sociol., 92, 6, 1287–1335, 1987.

      15. Milli, L., Rossetti, G., Pedreschi, D., Giannotti, F., Information diffusion in complex networks: The active/passive conundrum, in: Proceedings of International Conference on Complex Networks and their Applications, pp. 305–313, 2017.

      16. Sîrbu, A., Loreto, V., Servedio, V.D., Tria, F., Opinion dynamics: models, extensions and external effects, in: Participatory Sensing, Opinions and Collective Awareness, pp. 363–401, 2017.

      17. Sîrbu, A., Loreto, V., Servedio, V.D., Tria, F., Opinion dynamics with disagreement and modulated information. J. Stat. Phys., 151, 1, 218–237, 2013.

      18. Rossetti, G., Milli, L., Rinzivillo, S., Sîrbu, A., Pedreschi, D., Giannotti, F., NDlib: a Python library to model and analyze diffusion processes over complex networks. Int. J. Data Sci. Anal., 5, 1, 61–79, 2018.

      19. Staudt, C.L., Sazonovs, A., Meyerhenke, H., NetworKit: A tool suite for large-scale complex network analysis. Netw. Sci., 4, 4, 508–530, 2016.

      20. Hogan, B., Visualizing and interpreting Facebook networks, in: Analyzing Social Media Networks with NodeXL (2010), Morgan Kaufmann, Massachusetts.

      21. Gunawan, T.S., Abdullah, N.A.J., Kartiwi, M., Ihsanto, E., Social network analysis using python data mining, in: Proceedings of 8th International Conference on Cyber and IT Service СКАЧАТЬ