Survey on Link Prediction System

Authors(2) :-Kaustubh Mahajan, Prof. Mamta Bhamare

In the traditional link prediction problem, a snapshot of a social network is used as a starting point to predict, by means of graph-theoretic measures, the links that are likely to appear in the future. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. There are number of technique used for solving the link prediction problem. In this paper, discuss the survey on different techniques for link prediction problem. We also discuss cold start link problem. Also discussed the applications of machine learning to link prediction technique.

Authors and Affiliations

Kaustubh Mahajan
Department of Computer Engineering, MIT, Pune, Maharashtra, India
Prof. Mamta Bhamare
Department of Computer Engineering, MIT, Pune, Maharashtra, India

Social Network, link prediction, machine learning, feature selection.

  1. Shun-yaoWu, QiZhang, MeiWu, "Cold-start link prediction in multi-relational networks", in science direct, Volume 381, Issue 39, 17 October 2017.
  2. Hao Liao1, Mingyang Zhou1, Zong-wen Wei1, Rui Mao1,Alexandre Vidmer1, Yi- Cheng Zhang, "Hidden space reconstruction inspires link prediction in complex networks", arXiv:1705.02199 cs.SI], March 2017.
  3. Islam Elkabani and Roa A. Aboo Khachfeh, "Homophily-Based Link Prediction in the Facebook Online Social Network: A Rough Sets Approach", J. Intell. Syst. 2015; 24(4): 491-503.
  4. Manfang Wu, Zhanquan Wang, HaiLong Hu,"Friend Recommendation Algorithm for Online Social Networks Based on Location Preference", 3rd International Conference on Information Science and Control Engineering, 2016.
  5. Kyle Julian (kjulian3), Wayne Lu (waynelu), "Application of Machine Learning to Link Prediction", in December 16, 2016.
  6. Mingqiang Zhou, Rongchen Liu, Xin Zhao, Qingsheng Zhu, "Link Prediction Algorithm Based on Local Centrality of Common Neighbor Nodes Using Multi-Attribute Ranking", in the 12th International Conference on Computer Science & Education (ICCSE 2017).
  7. Jalili M, Orouskhani Y, Asgari M, Alipourfard N, Perc M. 2017 Link prediction in multiplex online social networks. R. Soc. open sci. 4: 160863.http://dx.doi.org/10.1098/rsos.160863.
  8. Santo Fortunato. 2010. Community detection in graphs. Physics Reports 486, 3 (2010), 75-174.
  9. Al Hasan M, Zaki MJ. 2011 A survey of link prediction in social networks. In Social network data analytics pp. 243-275. Berlin, Germany: Springer.
  10. Liben-Nowell D, Kleinberg J. 2007 The link-prediction problem for social networks. J.Am. Soc.Inf.Sci.Technol. 58, 1019-1031. (doi:10.1002/asi.20591)
  11. Hasan MA, Chaoji V, Salem S, Zaki M. 2006 Link prediction using supervised learning. In SDM Workshop of Link Analysis, Counterter rorismand Security,Maryland,22April
  12. Song HH, Cho TW, Dave V, Zhang Y. 2009 Scalable proximity estimation and link prediction in online social networks. In Internet Measurement Conf., Chicago,IL,4-6November.
  13. Sun Y, Barber R, Gupta M, Aggarwal CC, Han J. 2011 Co-author relationship prediction in heterogeneous bibliographic networks. In Int.Conf. On Advances in Social Networks Analysis and Mining, Taiwan,25-27July , pp. 121-128.
  14. Sun Y, Han J. 2012 Mining heterogeneous Information networks: principles and methodologies. Williston, VT: Morgan & Claypool.

Publication Details

Published in : Volume 3 | Issue 1 | January-February 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 428-432
Manuscript Number : CSEIT1831115
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Kaustubh Mahajan, Prof. Mamta Bhamare, "Survey on Link Prediction System", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.428-432 , January-February-2018.
Journal URL : http://ijsrcseit.com/CSEIT1831115

Article Preview

Follow Us

Contact Us