Читать книгу Social Network Analysis - Группа авторов - Страница 22

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 Management (CITSM), pp. 1–6, 2020.

22. Viard, T., Latapy, M., Magnien, C., Computing maximal cliques in link streams. Theor. Comput. Sci., 609, 245–252, 2016.

23. Housley, W., Procter, R., Edwards, A., Burnap, P., Williams, M., Sloan, L., Rana, O., Morgan, J., Voss, A., Greenhill, A., Big and broad social data and the sociological imagination: A collaborative response. Big Data Soc., 1, 2, 2053951714545135, 2014.

24. Casteigts, A., Flocchini, P., Quattrociocchi, W., Santoro, N., Time-varying graphs and dynamic networks, Int. J. Parallel Emergent Distrib. Syst., 27, 5, 387–408, 2012.

25. Ackland, R. and Zhu, J.J., Social network analysis, in: Innovations in Digital Research Methods, pp. 221–244, 2015.

26. Goldenberg, D., Social Network Analysis: From Graph Theory to Applications with Python. PyCon’19. arXiv preprint arXiv: 2102.10014, 2021

27. Sahneh, F.D., Vajdi, A., Shakeri, H., Fan, F., and Scoglio, C., GEMFsim: A stochastic simulator for the generalized epidemic modeling framework. J. Comput. Sci., 22, 36–44, 2017.

28. Van den Broeck, W., Gioannini, C., Gonçalves, B., Quaggiotto, M., Colizza, V., Vespignani, A., The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale. BMC Infect. Dis., 11, 1, 1–14, 2011.

29. Wilensky, U. and Tisue, S., Netlogo: A simple environment for modeling complexity, in: Proceedings of International conference on complex systems, vol. 21, pp. 16–21, 2004.

30. Chao, D.L., Halloran, M.E., Obenchain, V.J., Longini Jr., I.M., FluTE, a publicly available stochastic influenza epidemic simulation model. PloS Comput. Biol., 6, 1, e1000656, 2010.

31. Word, D.P., Abbott, G.H., Cummings, D., Laird, C.D., Estimating seasonal drivers in childhood infectious diseases with continuous time and discrete-time models. Proceedings of the American Control Conference, pp. 5137–5142, 20102010.

32. Zafarani, R., Abbasi, M., & Liu, H., Social media mining: An introduction. Cambridge: Cambridge University Press, 2014.

33. Sahu, B.P., Gouse, M., Pattnaik, C.R., Mohanty, S.N., MMFA-SVM: New bio-marker gene discovery algorithms for cancer gene expression. Materials Today: Proceedings, 2021, https://doi.org/10.1016/j.matpr.2020.11.617.

34. Arulkumar, N., Galety, M.G., Manimaran, A., CPAODV: Classifying and assigning 3 level preference to the nodes in VANET using AODV based CBAODV algorithm, in: Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2019. Learning and Analytics in Intelligent Systems, L. Jain, S.L. Peng, B. Alhadidi, S. Pal (Eds.), Springer, Cham, 2020, vol. 9, https://doi.org/10.1007/978-3-030-38501-9_42.

*Corresponding author: abhishek.dilip.bhambere@gmail.com