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Survey on Online Social Networks Analysis Concepts and Knowledge Discovery Techniques

by Noha Negm, Hany Mahgoub
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 14
Year of Publication: 2019
Authors: Noha Negm, Hany Mahgoub
10.5120/ijca2019918904

Noha Negm, Hany Mahgoub . Survey on Online Social Networks Analysis Concepts and Knowledge Discovery Techniques. International Journal of Computer Applications. 178, 14 ( May 2019), 12-21. DOI=10.5120/ijca2019918904

@article{ 10.5120/ijca2019918904,
author = { Noha Negm, Hany Mahgoub },
title = { Survey on Online Social Networks Analysis Concepts and Knowledge Discovery Techniques },
journal = { International Journal of Computer Applications },
issue_date = { May 2019 },
volume = { 178 },
number = { 14 },
month = { May },
year = { 2019 },
issn = { 0975-8887 },
pages = { 12-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number14/30597-2019918904/ },
doi = { 10.5120/ijca2019918904 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:50:22.071869+05:30
%A Noha Negm
%A Hany Mahgoub
%T Survey on Online Social Networks Analysis Concepts and Knowledge Discovery Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 14
%P 12-21
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the recent decade, the Online Social Network (OSN) has gained remarkable attention. Accessing to OSN sites such as Twitter, Facebook, LinkedIn and Google Plus; the most dominant social media in the world, through the internet and the web 2.0 technologies has become more comfortable. These days through these online social networks, it becomes very easy for anyone to meet the people of the same interests for learning and sharing precious information. Online Social Network Analysis (OSNA) is an essential and important technique to understand the social structure, social relationships and social behaviors of OSN. OSNA deals with the interaction between individuals by considering them as nodes of a network whereas their relations are mapped as network edges. Now, it has increased various challenges for the evolution of the web and simultaneously increased the dynamic changes in its structure so it became harder to manually analyze very broad OSN. This survey investigates the current progression in the field of knowledge discovery in OSNA and covers all basic techniques of Data, Text, and Web mining that are widely used for the exploration of the unstructured and structured data available on the OSNA. The targets for OSNA are mainly focused on resources from the web, such as content, structure, and user behaviors. The main goal of this paper is to introduce a roadmap for the researchers who are interesting on the topics of knowledge discovery techniques for discovering totally different trends in OSN data. Discussion of all the challenges that face researchers in OSNA is also included.

References
  1. Taprial V. and Kanwar P. 2012. Understanding Social Media. Published in Book Boon first edition. https://bookboon.com/en/understanding-social-media-ebook
  2. S. Tabassum, F. Pereira, S. Fernandes, J. Gama, “Social network analysis: An overview”, Journal of WIREs Data Mining Knowlge Discovery 2018.
  3. P. Chaudhary, S. Gupta, B.B. Gupta, V.S. Chandra, S. Selvakumar, M. Fire, R. Goldschmidt, Y. Elovici, S. Gangwar, M. Kumar, P.K. Meena, L. Sharma, “Auditing defense against XSS worms in online social network-based web applications”, in: Handbook of research on Modern Cryptographic Solutions for Computer and Cyber Security, Vol. 36, IGI Global, 2016, pp.216-245. No. 5, 1AD.
  4. H. Chen, R.H.L. Chiang, V.C. Storey, “Business intelligence and analytics: from big data to big impact”, Mis Q 36 (2012) 1165–1188.
  5. Chakrabarti, S. 2003. Mining the web: Discovering Knowledge from Hypertext Data. Morgan Kaufmann Publisher, USA.
  6. E. Raju, K. Sravanthi, “Analysis of social networks using techniques of web mining”, Journal of advanced research in computer science and software engineering, Vol. 2, Issue 10, 2012. pp.: 443-450.
  7. Zhu, J. J. H. 2007. Opportunities and Challenges for Network Analysis of Social and Behavioral Data. Seminar Series on Chaos, Control and Complex Networks City University of Hong Kong, Poly U University of Hong Kong & IEEE Hong Kong R&A/CS Joint Chapter.
  8. Borgatti, S., Everett, M. and Freeman, L. 2002. Ucinet for Windows: Software for Social Network Analysis. Harvard, MA: Analytic Technologies.
  9. It is freely available, for noncommercial use, at its homepage:
  10. http://vlado.fmf.uni-lj.si/pub/networks/pajek/
  11. B. Furh, “Handbook of Social Network Technologies and Applications”, Springer New York Dordrecht Heidelberg London, Springer Science+Business Media, LLC 2010.
  12. A.L. Kavanaugh, E. a. Fox, S.D. Sheetz, S. Yang, L.T. Li, D.J. Shoemaker, et al., “Social media use by government: From the routine to the critical”, Gov. Inf. Q. 29 (2012) 480–491.
  13. “Social Network Marketing: The basics”, http://www.labroots.com/Social_Networking_the_Basics.pdf, Aug 01, 2013.
  14. Jiawei H. and Kamber, M. 2001. Data Mining Concepts and Techniques, Morgan Kaufmann Publisher, New York, USA.
  15. Umadevi B., Sundar D., and Alli Dr.P. 2013. An Optimized Approach to Predict the Stock Market Behavior and Investment Decision Making using Benchmark Algorithms for Naive Investors. In proceedings of Computational Intelligence and Computing Research (ICCIC), 2013 IEEE International Conference on IEEE Explore Digital Library, pg1 -5.
  16. Cortizo, J., Carrero, F., Gomez, J., Monsalve, B., Puertas, E. 2009. Introduction to Mining SM. In Proceedings of the 1st International Workshop on Mining SM , 1 – 3.
  17. Kagdi, H., Collard, M. L., Maletic, J. I. 2007. A survey and taxonomy of approaches for mining software repositories in the context of software evolution. J. Softw. Maint. Evol.: Res. Pract, 19, 77-131.
  18. Richardson, M. and Domingos, P. 2001. The intelligent surfer: Probabilistic combination of link and content information in pagerank. In NIPS, pages 1441–1448.
  19. B. Umadevi, P. Surya, “A Review on Various Data Mining Techniques in Social Media”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 4, April 2017.
  20. Sorensen, L. 2009. User managed trust in social networking comparing Facebook, MySpace and LinkedIn. In Proceedings of 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic System Technology, (Wireless VITAE 09), 427–431.
  21. Y. Kano, W. A. Baumgartner, L. McCrohon, S. Ananiadou, K. B. Cohen, L. Hunter, and T. Tsujii, “Data mining: concept and techniques”, Oxford Journal of Bioinformatics 25(15), 2009.
  22. Yin, S.,Wang, G., Qiu, Y and Zhang,W. 2007. Research and implement of classification algorithm on web text mining. In Proceedings of 3rd International Conference on Semantics, Knowledge and Grid, 446–449.
  23. Tekiner, F., Aanaiadou, S., Tsuruoka, Y. and Tsuji, J. 2009. Highly scalable text mining parallel tagging application. In Proceedings of IEEE 5th International Conference on Soft Computing, Computing with Words and Perception in System Analysis, Decision and Control (ICSCCW), 1–4.
  24. T. Jo, “NTC (Neural Text Categorizer): neural network for text categorization”, International Journal of Information Science 2(2), 83–96, 2010.
  25. Ringel, M. M., Teevan, J. and Panovich, K. 2010.What do people ask their social networks, and why: a survey study of status message question & answer behavior. In Proceedings of International Conference on Human Factors in Computing Systems (CHI 10), 56–62.
  26. Li, J., Khan, S. U., Li, Q., Ghani, N., Bouvry, P. & Zhang, W. 2011a. Efficient data sharing over large-scale distributed communities. In Intelligent Decision Systems in Large-Scale Distributed Environments, Bouvry, P., Gonzalez-Velez, H. & Kolodziej, J. (eds). Springer, New York, NY, USA, 2011, pp. 110–128, ISBN: 978-3-642-21270-3.
  27. Chakrabarti, S. 2003. Mining the Web: Discovering Knowledge from Hypertext Data. Morgan Kaufmann Publishers, USA.
  28. Cooley, R., Mobasher, B. and Srivastave, J. 1997. Web Mining: Information and Pattern Discovery on the World Wide Web. In Proceedings of the 9th IEEE InternationalConferenc onToowithArtificial Intelligence, pp. 558-567, Newport Beach, CA, USA.
  29. Liu B. Carey MJ, Ceri S, eds. 2006. Web data mining: exploring hyperlinks, contents and usage data. In carey MJ, Ceri S, eds. Berlin: Springer.
  30. S. M. Goodreau, “Advances in exponential random graph (p*) models applied to a large social network”, Social Networks, 29(2), 231–248. 2007.
  31. J. M. Kleinberg, “Authoritative sources in a hyperlinked environment”, Journal of the ACM (JACM), 46(5), 604–632. 1999.
  32. Biswal, B. 2008. Web site optimization through mining user navigational patterns, web engineering and application. New Delhi: Narosa Publishing House.
  33. Li, F. 2008. Extracting structure of web site based on hyperlink analysis. In proceedings of fourth international conference on wireless communication. Networking and Mobile Computing, 1–4.
  34. X. Fang, and O. Sheng, “LinkSelector: Web mining approach to hyperlink selection for web portals”, Journal of ACM Transactions on Internet Technology, 4(2), 209–237.2004.
  35. Lento, T., Welser, H. T., Gu, L., and Smith, M. 2006. The ties that blog: Examining the relationship between social ties and continued participation in the wallop weblogging system. In proceedings of 3rd Annual Workshop on the Weblogging ecosystem (Vol. 12).
  36. Nina, S. P., Rahaman, M., Bhuiyan, K., and Khandakar E. 2009. Pattern Discovery Of Web Usage Mining. In proceedings of International Conference on Computer Technology and Development, Vol. 1.
  37. R.Kosala, and H. Blockeel, “Web mining research: A survey”, Journal of ACM SIGKDD Explorations Newsletter, 2(1), 1–15. 2000
  38. Mladenic, D., Grobelnik, M. 1999. Predicting content from hyperlinks. In Proceedings of the 16th International ICML99 Workshop on Machine Learning in Text Data Analysis (pp. 109–113).
  39. B. Berendt, “Using site semantic to analyze, visualize and support navigation”, Journal of Data Mining and Knowledge Discovery, 6, 37–59. 2002.
  40. Dai, H. Mobasher, B. 2003. A road map to more effective Web personalization; Integrating domain knowledge with Web usage mining. In Proceedings of the International Conference on Internet Computing (IC 2003), Las Vegas, Nevada.
  41. Oberle, D., Berendt, B., Hotho, A., Gonzalez, J. 2003. Conceptual user tracking. Lecture notes on artificial intelligence, Vol. 2663, pp. 155–164.
  42. M. Spiliopoulou, and C. Pohle, “Data mining for measuring and improving the success of Web sites”, Journal of Data Mining and Knowledge Discover, 5(1–2), 85–114. 2001
  43. Agrawal, R. and Srikant, R. 2017. Fast algorithms for mining association rules. In proceedings of International Conference of VLDB, pp. 487– 499, 1994. V. Bhatia, R. Rani, “A parallel fuzzy clustering algorithm for large graphs using Pregel”, Journal of Expert System with Applications, 78(c):135-144.
Index Terms

Computer Science
Information Sciences

Keywords

Social Network Online Social Network Analysis Knowledge Discovery Data Mining Text Mining Web Mining