Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 03, March 2024

Call for Paper

Vol 11, Issue 03, March 2024

Indexing

Detection and Analysis of Influence by Renowned Leaders in Online Social Networks

Author : Sr.SujathaYeruva 1

PDF

Date of Publication :21st September 2017

Abstract: Social networks (SNs) are part and parcel of human life: inhabitants progress, influence and are influenced by their acquaintances. The quantity of knowledge accessible on SNs is gigantic with the advent of World Wide Web, allowing the qualitative exploration of information. Social network analysis (SNA) has been gaining thought from divergent areas like social science, economics, psychology, biology. The swift rise of the net SN sites and their publically procurable knowledge attaining API’s has crystallized the prosperity of SNA analysis. Tracing potent users and their influence is one among the trendiest topics of SNA. Contemporary exploration of associations among the members of a SN reveals that through direct contacts individuals influence indirect ones. Substantive data concerning the prestigious users and also the ability to predict them could also be leveraged for many applications. This analysis work employs Pareto Front function to mark the outstanding leaders that is followed by an empirical analysis of the results of their dependable influence

Reference :

    1. Leila Weitzel, Paulo Quaresma, José Palazzo M. de Oliveira, Measuring node importance on Twitter microblogging. WIMS'12, June 13-15, 2012 Craiova, Romania Copyright © 2012 ACM 978-1-4503-0915- 8/12/06
    2. Wasserman, S. 1999. Social network analysis: methods and applications. Cambridge University Press
    3. Tian Zhu, Bai Wang, Bin Wu, Chuanxi Zhu. Maximizing the spread of influence ranking in social networks. 0020-0255/ © 2014 Elsevier Inc. Information Sciences Volume 278, 10 September 2014, Pages 535– 544
    4. Albert, R. and Barabási, A.L. 2002. Statistical mechanics of complex networks. Reviews of modern physics.74, 1 (2002), 47. [5] Kim, W. et al. 2010. On social Web sites. Information Systems. 35, 2 (Apr. 2010), 215-236.
    5. R. Guimera, M. Sales-Pardo, L.A.N. Amaral, Classes of complex networks defined by role-to-role connectivity profiles, Nat. Phys. (bfseries) (2007) 63–69 (January).
    6. M. Chiang, C. Tsai, C. Yang, A time-efficient pattern reduction algorithm for k-means clustering, Inform. Sci. 181 (4) (2011) 716–731
    7. Kempe, J. Kleinberg, É. Tardos, Maximizing the spread of influence through a social network, in: Proceedings of the 9th ACM SigKDD Conference on Knowledge Discovery and Data Mining. ACM, Washington, DC, USA, 2003, pp. 137–146.
    8. J. Coleman. Foundations of Social Theory. Harvard, 1990
    9. Yoonhyuk Jung, Understanding the Role of Sense of Presence and Perceived Autonomy in Users’ Continued Use of Social Virtual Worlds. Journal of Computer-Mediated Communication 16 (2011) 492–510 © 2011 International Communication Association
    10. Sujatha Yeruva, A Study on Social Influence Analysis in Social Networks, Amrita International Conference of Women in Computing (AICWIC’13) Proceedings published by International Journal of Computer Applications® (IJCA)
    11. Mattson, C. A., and Messac, A., “Pareto Frontier Based Concept Selection under Uncertainty, with Visualization,” Kluwer Academic Publishers – Special Issue on Multidisciplinary Design Optimization, Invited (refereed) Paper, OPTE: Optimization and Engineering, Vol. 6, No. 1, March 2005, pp. 85-115.
    12.  R. E. Steuer, Multiple Criteria Optimization, Theory Computations and Applications, John Wiley & Sons, Inc.: New York, 1986.
    13.  A. Belegundu and T. Chandrupatla, Optimization Concepts and Applications in Engineering, Prentice Hall: New Jersey, 1999.
    14. K. M. Miettinen, Nonlinear Multiobjective Optimization, International Series in Operations Research & Management Science. Kluwer Academic Publishers, 1999.
    15. Pareto, Cour d’Economie Politique, Geneve: Librarie Droz. the 1st edition in 1896, 1964.
    16. Pareto front :By Yi Cao at Cranfield University, 31 October 2007
    17. Pareto front function: Definition, history. Website accessed August 15, 2013 from http://en.wikipedia.org/wiki/Pareto_efficiency
    18. Carlos A. Coello Coello. Basic Concepts
    19. Facebook. Facebook statistics. Available at http://newsroom.fb.com/company-info/ Last Accessed: 21-Jul-2014
    20. A global business that supplies amenities to the technology industry. Website accessed September 20, 2013 fromhttp://www.techweb.com
    21. Becker, J. A. H., & Stamp, G. H. (2005). Impression management in chat rooms: A grounded theory model. Communication Studies, 56(3),243–260.
    22. Tian Zhu, Bai Wang, Bin Wu, Chuanxi Zhu. Maximizing the spread of influence ranking in social networks. 0020-0255/ © 2014 Elsevier Inc. Information Sciences Volume 278, 10 September 2014, Pages 535– 544.
    23. Malaika Brengman, Farhod P. Karimov, The effect of web communities on consumers’ initial trust in B2C e-commerce websites, Management Research Review Vol. 35 No. 9, 2012 pp. 791-817
    24. Huiyuan Zhang, Thang N. Dinh, and My T. Thai, Maximizing the Spread of Positive Influence in Online Social Networks, 1063-6927/13 ©2013 IEEE, DOI 10.1109/ICDCS.2013.37.
    25. Huiju Park, Hira Cho, Social network online communities: information sources for apparel shopping, Journal of Consumer Marketing 29/6(2012) 400–411
    26. Noni Keys, Dana C. Thomsen and Timothy F. Smith, Opinion leaders and complex sustainability issues, Management of Environmental Quality: An International Journal Vol. 21 No. 2, 2010 pp. 187-197
    27. Flavio L. Pinheiro, Marta D. Santos, Francisco C. Santos, Jorge M. Pacheco. The Origin of Peer Influence in Social Networks. 2014.
    28. Linyuan Lu¨ , Yi-Cheng Zhang, Chi Ho Yeung, Tao Zhou 2011 Lu¨ et al. Leaders in Social Networks, the Delicious Case. PLoS ONE |www.plosone.org, June 2011 | Volume 6 | Issue 6 | e21202
    29. Ulrike Pfeil, Panayiotis Zaphiris, Stephanie Wilson. The role of message-sequences in the sustainability of an online support community for older people. Journal of Computer-Mediated Communication 15 (2010) 336–363 © 2010 International Communication Association
    30. Eytan Bakshy, Itamar Rosenn, The Role of Social Networks in Information Diffusion, WWW 2012, April 16–20, 2012, Lyon, France.ACM 978-1-4503-1229- 5/12/04.
    31. Asim Ansari, OdedKoenigsberg, Florian Stahl. Modelling Multiple Relationships in Social Networks. Journal of Marketing Research Article Post print © 2011, American Marketing Association
    32. Keith S. Coulter, Anne Roggeveen, “Like it or not” Consumer responses to word-of-mouth communication in on-line social networks, Management Research Review Vol. 35 No. 9, 2012 pp. 878-899
    33. Shweta Garg, Sanjeev Kumar, Modeling and Analyzing Information Diffusion Behaviour of Social Networks, 978-1-4799-2900-9/14/ ©2014 IEEE, 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT).
    34. Amit Goyal, Francesco Bonchi, Laks V. S. Lakshmanan. Discovering Leaders from Community Actions. CIKM’08, October 26–30, 2008,Napa Valley, California, USA.
    35. Reihaneh Rabbany Khorasgani, Jiyang Chen, Osmar R. Zaïane. Top Leaders Community Detection Approach in Information Networks.KDD’10 July 2010, Washington, DC, USA.
    36. D. M. Akbar Hussain. Identifying Leader or Follower using a Binary Approach. Proceedings of the International Multi Conference of Engineers and Computer Scientists 2010 Vol I, IMECS 2010, March 17- 19, 2010, Hong Kong.
    37. Lars Backstrom, Dan Huttenlocher, Jon Kleinberg, Xiangyang Lan. Group Formation in Large Social Networks: Membership, Growth, and Evolution, KDD’06, August 20–23, 2006, Philadelphia, Pennsylvania, USA. Copyright 2006 ACM 1-59593-339- 5/06/0008
    38. Sun J, Tang J. A Survey of Models and Algorithms for Social Influence Analysis. In Social Network Data Analytics. Springer US, New York NY; pp. 177–214, 2011.
    39. M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In Proc. of ACM SIGKDD, 2002.
    40. D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In Proc. of ACM SIGKDD, 2003.
    41.  D. Watts. A simple model of global cascades on random networks. PNAS, 99:5766–5771, 2002.
    42. J. Leskovec, L. Adamic, and B. Huberman. The dynamics of viral marketing. In Proc. of ACM Electronic Commerce, 2006.
    43. Social Network Analysis (SNA). A tutorial on concepts and methods. Website accessed October 9, 2013 from http://www.slideshare.net/gcheliotis/social-networkanalysis-32730
    44. Mirjam Wattenhofer, Roger Wattenhofer, Zack Zhu,” The YouTube Social Network” © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org)
    45. Seelye, Katharine Q. (June 13, 2007). "New Presidential Debate Site? Clearly, YouTube". The New York Times. Archived from the original on January 19, 2014.
    46. Lidsky, David (February 1, 2010). "The Brief But Impactful History of YouTube". Fast Company. Archived from the original on February 5, 2014.
    47. Heffernan, Virginia (November 14, 2008). "Clicking and Choosing". The New York Times Magazine. Archived from the original on February 19, 2014.
    48. Alan Mislove; Massimiliano Marcon; Krishna P. Gummadi; Peter Druschel; Bobby Bhattacharjee. Measurement and Analysis of Online Social Networks. In Proc. mislove-2007-socialnetworks, 5th ACM/Usenix Internet Measurement Conference (IMC'07), San Diego, CA , October 2007

Recent Article