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General Information
    • ISSN: 2010-3697
    • Frequency: Bimonthly
    • DOI: 10.7763/IJMO
    • Editor-in-Chief: Prof. Adrian Olaru
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: Engineering & Technology Digital Library, ProQuest, Crossref, Electronic Journals Library, Google Scholar, EI (INSPEC, IET).
    • E-mail ijmo@iacsitp.com
Editor-in-chief
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2014 Vol.4(1): 21-24 ISSN: 2010-3697
DOI: 10.7763/IJMO.2014.V4.341

An Experimental Comparison of the Link Prediction Techniques in Social Networks

D. Sharma, U. Sharma, and Sunil Kumar Khatr
Abstract—Link prediction is a very well studied problem as it has applications in many different areas. Many algorithms have been presented in the literature for the Link prediction problem. The general for of the problem is that given the topology of graph G at a certain time t, we need to predict the topology of the graph G at time t’ where t’ > t assuming that the number of nodes does not change. The techniques used for Link prediction are categorized into three types: Nodes based techniques, Link based techniques and Path based techniques. Then there are other techniques that use meta-approaches such as.....which are based on the basic techniques. In this paper we conduct a survey of all the existing Link Prediction techniques to the best of our knowledge and perform an experimental comparison of these techniques. We use real social network data for the testing.

Index Terms—Link prediction, social networks.

The authors are with Amity Institute of Information Technology, Amity University Uttar Pradesh, Noida, India (e-mail: {dsharma10, usharma}@amity.edu, sunilkkhatri@gmail.com).

[PDF]

Cite:D. Sharma, U. Sharma, and Sunil Kumar Khatr, "An Experimental Comparison of the Link Prediction Techniques in Social Networks," International Journal of Modeling and Optimization vol. 4, no. 1, pp. 21-24, 2014.

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