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Real Time Social Network Data Analysis for Community Detection

by Mohammad Sarwar Jahan Morshed, Akinul Islam Jony
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 139 - Number 6
Year of Publication: 2016
Authors: Mohammad Sarwar Jahan Morshed, Akinul Islam Jony
10.5120/ijca2016907645

Mohammad Sarwar Jahan Morshed, Akinul Islam Jony . Real Time Social Network Data Analysis for Community Detection. International Journal of Computer Applications. 139, 6 ( April 2016), 1-5. DOI=10.5120/ijca2016907645

@article{ 10.5120/ijca2016907645,
author = { Mohammad Sarwar Jahan Morshed, Akinul Islam Jony },
title = { Real Time Social Network Data Analysis for Community Detection },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 139 },
number = { 6 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume139/number6/24491-2016907645/ },
doi = { 10.5120/ijca2016907645 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:40:10.617184+05:30
%A Mohammad Sarwar Jahan Morshed
%A Akinul Islam Jony
%T Real Time Social Network Data Analysis for Community Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 139
%N 6
%P 1-5
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In WWW becomes a widely used platform for different social networks and social medias for the social communication. This platform becomes the oasis of a huge amount of data. Therefore, this data repository draws tremendous attention from corporate, government, NGOs, social workers, politician, etc. to either promote their products or to convey their message to the targeted community. But identification of community structure and social graph becomes a challenging issue for the social network researcher and graph theory researchers since the pervasive usage of instant messaging systems and fundamental shift in publishing contents in these social medias. Although a lot of attention has been given by the researcher to introduce several algorithms for identifying the community structure, most of them are not suitable for dealing with the large scale social network data in real time. This paper presents a model for community detection from social graph using the real time data analytic. In this paper, we introduce data analytic algorithms that can analysis contextual data. These algorithms can analyze large scale social interaction data and can detect a community based on the user supplied threshold value for community detection. Experiment result shows that the proposed algorithms can identify expected number meaningful communities from the social graph.

References
  1. Wikipedia, "List of social network site", http://en.wikipedia.org/wiki/List_of_social_networking_websites", 2014.
  2. Semenov, A. and Veijalainen, J. A modeling framework for social media monitoring, in IJWET, in press, 2012.
  3. Doan, S, Ohno-Machado, L., and Collier, N. 2012. Enhancing Twitter Data Analysis with Simple Semantic Filtering: Example in Tracking Influenza-Like Illnesses. In IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology (HISB). Ohno-Machado, Lucila, Jiang, X. (Eds.), California, USA: IEEE Computer Society, pp. 62 –71.
  4. S. Earle, P., C. Bowden, D. and Guy, M. 2011. Twitter earthquake detection: earthquake monitoring in a social world. In Annals of Geophysics, vol. 54(6), pp. 708-715.
  5. O. Larsson, A. and Moe, H. 2012. Studying political microblogging: Twitter users in the 2010 Swedish election campaign. In New Media Society vol. 14(5), pp. 729–747.
  6. Skoric, M., Poor, N., Achananuparp, P., P. Lim, E. and Jiang, J. 2012. Tweets and Votes: A Study of the 2011 Singapore General Election. In 45th Hawaii International Conference on System Science (HICSS), 2012, pp. 2583 – 2591.
  7. Aliprandi, C. and Marchetti, A. 2011. Introducing CAPER, a Collaborative Platform for Open and Closed Information Acquisition, Processing and Linking. In Stephanidis, C. (Ed.), HCI International 2011 – Posters’ Extended Abstracts. Berlin, Heidelberg: Springer, pp. 481–485.
  8. Heinonen, O., Hätönen, K. and Klemettinen, M. 1996. WWW Robots and Search Engines. In Seminar on Mobile Code No. TKO-C79), Helsinki University of Technology, Department of Computer Science.
  9. Thelwall, M. 2011. A web crawler design for data mining. In Journal of Information Science, vol. 27(5), pp. 319–325.
  10. Yih, W., Chang, P. and Kim, W. 2004. Mining Online Deal Forums for Hot Deals. In Zhong, N., Tirri, H., Yao, Y., Zhou, L., Liu, J., Cercone, N. (Eds.), Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence, WI ’04. Washington, DC, USA: IEEE Computer Society, pp. 384–390.
  11. Bergholz, A. and Childlovskii, B. 2003. Crawling for domain-specific hidden Web resources. In Santucci, G., Klas, W., Bertolotto, M., Calero, C., Baresi, L. (Eds.), Proceedings of the Fourth International Conference on Web Information Systems Engineering, 2003. Washington, DC, USA: IEEE Computer Society, pp. 125 – 133.
  12. Duda, C., Frey, G., Kossmann, D., R. Matter, D. and Zhou, C. 2009. AJAX Crawl: Making AJAX Applications Searchable. In Ioannidis, Y.E., Lun Lee, D., Ng, R.T. (Eds.), Proceedings of the 2009 IEEE International Conference on Data Engineering, ICDE ’09. Washington, DC, USA: IEEE Computer Society, pp. 78–89.
  13. Peng, L. and Wen-Da, T. 2010. A focused web crawler face stock information of financial field. In Zhou, M. (Ed.), 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS), pp. 512 –516.
  14. Y. Yang, S. and L. Hsu, C. 2009. Ontology-supported web crawler for information integration on call for papers. In Chan, P. (Ed.), International Conference on Machine Learning and Cybernetics, pp. 3354 –3360.
  15. Girvan, M. and E. J. Newman, M. 2002. Community structure in social and biological networks. In PNAS, vol. 99(12), pp. 7821–7826.
  16. Kernighan, B. and Lin, S. 1970. An Efficient Heuristic Procedure for Partitioning Graphs. In The {B}ell system technical journal, vol. 49(1), pp. 291–307.
  17. E. J. Newman, M. 2004. Fast algorithm for detecting community structure in networks. In Phys. Review, vol. 69(6).
  18. Papadopoulos, S., Kompatsiaris, Y., Vakali, A. and Spyridonos, P. 2011. Community detection in Social Media. In Data Mining and Knowledge Discovery, vol. 24(3), pp. 515–554.
Index Terms

Computer Science
Information Sciences

Keywords

Real time data Social Network Community Detection Big data Data Analytic.

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