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Similarity Scores Evaluation in Social Networking Sites

Publication Type : Book Chapter

Publisher : Advances in Intelligent Systems and Computing.

Source : Advances in Intelligent Systems and Computing, Volume 398, p.601 - 614 (2016)

Url :

ISBN : 9788132226727

Campus : Coimbatore

School : School of Engineering

Center : Center for Computational Engineering and Networking

Department : Computer Science

Verified : Yes

Year : 2016

Abstract : In today’s world, social networking sites are becoming increasingly popular. Often we find suggestions for friends, from such social networking sites. These friend suggestions help us identify friends that we may have lost touch with or new friends that we may want to make. At the same time, these friend suggestions may not be that accurate. To recommend a friend, social networking sites collect information about user’s social circle and then build a social network based on this information. This network is then used to recommend to a user, the people he might want to befriend. FoF algorithm is one of the traditional techniques used to recommend friends in a social network. Delta-SimRank is an algorithm used to compute the similarity between objects in a network. This algorithm is also applied on a social network to determine the similarity between users. Here, we evaluate Delta-SimRank and FoF algorithm in terms of the friend suggestion provided by them, when applied on a Facebook dataset. It is observed that Delta-SimRank provides a higher precise similarity score because it considers the entire network around a user.

Cite this Research Publication : A. Ravindran, Kumar, P. N., and Subathra P., “Similarity Scores Evaluation in Social Networking Sites”, in Advances in Intelligent Systems and Computing, vol. 398, 2016, pp. 601 - 614.

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