CFP last date
20 May 2024
Reseach Article

A Survey on Social Network Analysis and their Issues

Published on April 2017 by Amit Aylani, Nikita Goyal
National Conference on Contemporary Computing
Foundation of Computer Science USA
NCCC2016 - Number 3
April 2017
Authors: Amit Aylani, Nikita Goyal
daeeeedb-e2ab-47b2-b545-7577aabf2803

Amit Aylani, Nikita Goyal . A Survey on Social Network Analysis and their Issues. National Conference on Contemporary Computing. NCCC2016, 3 (April 2017), 25-28.

@article{
author = { Amit Aylani, Nikita Goyal },
title = { A Survey on Social Network Analysis and their Issues },
journal = { National Conference on Contemporary Computing },
issue_date = { April 2017 },
volume = { NCCC2016 },
number = { 3 },
month = { April },
year = { 2017 },
issn = 0975-8887,
pages = { 25-28 },
numpages = 4,
url = { /proceedings/nccc2016/number3/27353-6357/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Contemporary Computing
%A Amit Aylani
%A Nikita Goyal
%T A Survey on Social Network Analysis and their Issues
%J National Conference on Contemporary Computing
%@ 0975-8887
%V NCCC2016
%N 3
%P 25-28
%D 2017
%I International Journal of Computer Applications
Abstract

Social network has become an important part of every individual's daily routine. Social Networking Sites (SNS) like Twitter, Facebook, LinkedIn and Google+ are user interfaces for social network which can be accessed through internet and web 2. 0 technologies. It provides a platform for its users, where they connect, communicate, share, update etc. People are developing more interest for social networks as there is dependency for information, news, daily updates and psychology of other humans on a generalized scenario is easily available with a ease of access. With this increasing use and interest of any SNS a lot more enhancement and more utility can be provided for its users which is a crucial and a tedious area of interest for researchers with growing trend and demand. In this paper a attempt is made to summarize basic ideas behind a Social Network and working of a SNS with some existing work done and areas where there is scope of improvement by categorizing the research topics for a online social network.

References
  1. A. Hanneman and M. Riddle, 2005 Introduction to social network methods, online at http://faculty. ucr. edu/hanneman/nettext.
  2. Yuheng Hu, Lydia Manikonda and Subbarao Kambhampati, 2014. What We Instagram: A First Analysis of Instagram Photo Content and User Types. Weblogs and Social Media. In 8th International AAAI Conference.
  3. Charu C. Aggarwal, 2011. Social Network Data Analytics. Springer New York Dordrecht Heidelberg. Library of Congress Control Number.
  4. T. Y. Berger-Wolf and J. Saia, 2006. A framework for analysis of dynamic social networks. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining.
  5. Jaewon Yang, Julian McAuley and Jure Leskovec, 2013. Community Detection in Networks with Node Attributes In IEEE 13th International Conference on Data Mining.
  6. Liaoruo Wang, Tiancheng Lou, Jie Tang and John E. Hopcrof, 2011. Detecting Community Kernels in Large Social Networks. In IEEE 11th International Conference on Data Mining.
  7. Xutao Li, Michael K. Ng, and Yunming Ye, 2014. MultiComm: Finding Community Structure in Multi-Dimensional Networks. In IEEE Transactions on Knowledge and Data Engineering.
  8. M. E. J. Newman and M. Girvan, 2004. Finding and Evaluating Community Structure in Networks. In Physical Review E, 69:026113.
  9. Vasavi Akhila Dabeeru, 2014. User Profile Relationships Using String Similarity Metrics in Social Networks. Social and Information Networks (cs. SI).
  10. O. Tsur and A. Rappoport. , 2012. What's in a hashtag?: content based prediction of the spread of ideas in micro blogging communities. In Proceedings of the 5th ACM international conference on Web search and data mining.
  11. Zangerle, E. , Gassler, W. , 2011. Recommending #-tags in twitter. In Proceedings of the CEUR Workshop.
  12. Rodrigues B. , G. A. , Silva, I. S. , Zaki, M. , Meira Jr, W. , Prates, R. O. , & Veloso, A. , 2012. Characterizing the effectiveness of twitter hash tags to detect and track online population sentiment. Proceedings of ACM annual conference extended abstracts on Human Factors.
  13. SOCNETV: Social Network Analysis and Visualization Software http://socnetv. sourceforge. net.
  14. NodeXL: Network Overview, Discovery and Exploration for Excel https://nodexl. codeplex. com
  15. G. Vinodhini and RM. Chandrasekaran, 2012. Sentiment Analysis and Opinion Mining: A Survey, In International Journal of Advanced Research in Computer Science and Software Engineering.
  16. Asso Hamzehei, Mohammad Ebrahimi, Elahe Shafiei Bavani, Raymond K. Wong, and Fang Chen, 2015. Scalable Sentiment Analysis for Microblogs based on Semantic Scoring. IEEE International Conference on Services Computing.
  17. Theresa Wilson, Janyce Wiebe and Paul Hoffmann, 2005. Recognizing contextual polarity in phrase-level sentiment analysis. Proceedings of the conference on Human Language Technology and Empirical Methods. In Natural Language Processing.
  18. Krishna B. Kansara Narendra M. Shekokar, 2016. Security against sybil attack in social network. International Conference on Information Communication and Embedded System.
  19. H. Yu, M. Kaminsky, P. B. Gibbons, and A. Flaxman, 2006. SybilGuard: Defending against Sybil Attacks via Social Networks. Proceeding ACM SIGCOMM.
  20. H. Yu, P. B. Gibbons, M. Kaminsky, and F. Xiao, 2008. SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks. IEEE Symposium Security and Privacy.
  21. W. Wei, F. Xu, C. C. Tan, and Q. Li, 2012. SybilDefender: Defend against Sybil Attacks in Large Social Networks. Proceeding IEEE INFOCOM.
Index Terms

Computer Science
Information Sciences

Keywords

Social Network Social Networking Sites Community Social Connection.