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Empirical Analysis of Traditional Link Prediction Methods
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 121 - Number 2 |
| Year of Publication: 2015 |
| Authors: Jagadishwari.v, Umadevi. V |
10.5120/21509-4296
|
Jagadishwari.v, Umadevi. V . Empirical Analysis of Traditional Link Prediction Methods. International Journal of Computer Applications. 121, 2 ( July 2015), 1-5. DOI=10.5120/21509-4296
Abstract
Online Social Networks are growing exponentially due to which a lot of researchers are working on Social Network analysis. Link Prediction is a task of predicting new links that may occur in future in the social network. The link prediction problem has generated a lot of interest due its widespread applicability across many domains. We conducted a study on the different methods that have been developed for link prediction. In most of these methods, the social network is modeled as a graph, and the links are predicted based on the similarities between two nodes. We have chosen seven widely used similarity methods in our study. We found that on the simulated data sets, Sorenson index method and Jaccard coefficient method performed well when compared to other methods.
References
- Mark EJ Newman. Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 101(suppl 1):5200–5205, 2004.
- PengWang, BaoWen Xu, YuRongWu, and Xiaoyu Zhou. Link prediction in social networks: the state-of-the-art. Science China Information Sciences, pages 1–38, 2014.
- Linyuan L¨u and Tao Zhou. Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications, 390(6):1150–1170, 2011.
- Tsung-Ting Kuo, Rui Yan, Yu-Yang Huang, Perng-Hwa Kung, and Shou-De Lin. Unsupervised link prediction using aggregative statistics on heterogeneous social networks. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 775–783. ACM, 2013.
- Milen Pavlov and Ryutaro Ichise. Finding experts by link prediction in co-authorship networks. Finding Experts on the Web with Semantics, 290:42–55, 2007.
- Steve Ressler. Social network analysis as an approach to combat terrorism: Past, present, and future research. Homeland Security Affairs, 2(2):1–10, 2006.
- Stuart Koschade. A social network analysis of jemaah islamiyah: The applications to counterterrorism and intelligence. Studies in Conflict & Terrorism, 29(6):559–575, 2006.
- David Liben-Nowell and Jon Kleinberg. The linkprediction problem for social networks. Journal of the American society for information science and technology, 58(7):1019–1031, 2007.
- Mark EJ Newman. Clustering and preferential attachment in growing networks. Physical Review E, 64(2):025102, 2001.
- Paul Jaccard. Etude comparative de la distribution florale dans une portion des Alpes et du Jura. Impr. Corbaz, 1901.
- Thorvald Sorensen. A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on danish commons. Biol. Skr. , 5:1–34, 1948.
- Salton, Gerard, McGill, and Michael J. Introduction to modern information retrieval. 1983.
- Lada A Adamic and Eytan Adar. Friends and neighbors on the web. Social networks, 25(3):211–230, 2003.
- Albert-L´aszl´o Barab´asi and R´eka Albert. Emergence of scaling in random networks. 286(5439):509–512, 1999.
- Leo Katz. A new status index derived from sociometric analysis. Psychometrika, 18(1):39–43, 1953.
- Zhifeng Bao, Yong Zeng, and YC Tay. Sonlp: Social network link prediction by principal component regression. In Advances in Social Networks Analysis and Mining (ASONAM), IEEE/ACM International Conference on, pages 364–371. IEEE, 2013.
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