Abstract
Huge amount of data is being produced by online social interaction among people. This data can be represented by graphs where nodes represent individuals and the connecting edges depicts their interaction. In this research we analyze a social network of individuals to understand social structure among them. Online interaction has become integral part of life nowadays, large amount of research is available in analyzing these social interactions. However, previous research in this area lacks in identifying social structure among people using email data and graph theory based techniques. In this regard, a model for analyzing social structure of the community is presented in this research. An algorithm is designed to extract social structure of the community named Socio Rank. We crawled a large real world email interaction data in this research and extensive graph theory based experiments are performed to cluster the graph among different communities. Subsequently, widespread analysis was performed to study the hierarchy of social structure in the society. The experiments revealed multiple clusters in the group related to individuals fulfilling different roles in the community. We correlated the connection properties of individual nodes with the behavior of people in the society. Graph based Harel-Koren layout technique and Girvan-Newman clustering algorithm was used for the analysis of the underlying extracted communities. A hierarchical social classification was identified among individuals in the community. Our work of social structure extraction on a controlled community can be correlated with the society at large.
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References
Li, H.: Centrality analysis of online social network big data. In: 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA) (2018)
De Jaegher, H., et al.: Grasping intersubjectivity: an invitation to embody social interaction research. Phenomenol. Cogn. Sci. 16(3), 491–523 (2017)
Durlauf, S.N., Blume, L.: The New Palgrave Dictionary of Economics, vol. 6. Palgrave Macmillan Basingstoke, London (2008)
Staab, M.: The Formation of Social Groups Under Status Concern (2018)
Drázdilová, P., et al.: Creation of students’ activities from learning management system and their analysis. In: International Conference on Computational Aspects of Social Networks, CASON 2009. IEEE (2009)
Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)
Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)
Leskovec, J., Mcauley, J.J.: Learning to discover social circles in ego networks. In: Advances in Neural Information Processing Systems (2012)
Clauset, A., Newman, M.E., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)
Laine, M.S.S., Ercal, G., Luo, B.: User groups in social networks: an experimental study on YouTube. In: 2011 44th Hawaii International Conference on System Sciences (HICSS). IEEE (2011)
Ogata, H., et al.: Computer supported social networking for augmenting cooperation. Comput. Support. Coop. Work (CSCW) 10(2), 189–209 (2001)
Flake, G.W., et al.: Self-organization and identification of web communities. Computer 35(3), 66–70 (2002)
Hopcroft, J., et al.: Natural communities in large linked networks. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2003)
Bajwa, I.S., Sarwar, N., Naeem, A.: Generating express data models from SBVR. Proc. Pak. Acad. Sci. 53(4A), 381–389 (2016)
Bilal, M., Sarwar, N., Bajwa, S., Nasir, A., Rafiq, W.: New work flow model approach for test case generation of web applications. Bahria Univ. J. Inf. Comun. Technol. 9(2017), 28–33 (2016)
Bajwa, S., Sarwar, N.: Automated generation of EXPRESS-G models using NLP. Sindh Univ. Res. J. (Sci. Ser.) 48(1), 5–12 (2016)
Saeed, S., Sarwar, N., Bilal, M.: Efficient requirement engineering for small scale project by using UML. In: The Sixth International Conference on Innovative Computing Technology (INTECH 2016), pp. 662–666 (2016)
Sajjad, R., Sarwar, N.: NLP based verification of a UML class model. In: 2016 6th International Conference on Innovative Computing Technology, INTECH 2016, pp. 30–35 (2017). https://doi.org/10.1109/INTECH.2016.7845070
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Rafique, W., Khan, M., Sarwar, N., Sohail, M., Irshad, A. (2019). A Graph Theory Based Method to Extract Social Structure in the Society. In: Bajwa, I., Kamareddine, F., Costa, A. (eds) Intelligent Technologies and Applications. INTAP 2018. Communications in Computer and Information Science, vol 932. Springer, Singapore. https://doi.org/10.1007/978-981-13-6052-7_38
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DOI: https://doi.org/10.1007/978-981-13-6052-7_38
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