Cherry Ahmed
ABSTRACT
Studying social networks and the ties connecting people in those networks has attracted many researchers. Social networks like Facebook, Twitter and Flickr require efficient and accurate methods to recommend friends to their users in the network. Several algorithms have been developed to recommend friends or predict likelihood of future links. Link Prediction algorithms utilize local features of the network in the neighborhood of the two nodes in question, or use global features like path structure of the whole network. New algorithms tend to combine both in order to achieve the best results such as FriendTNS that takes into account the degrees of the nodes, and the direct links between them. This paper extends FriendTNS such that it takes the strength of the tie between two users into account. The strength of the tie is represented by the interaction that takes place between two users. In order to evaluate the correctness of the proposed model, it has been applied on a real dataset of 2.974k users on the Twitter social network. The proposed model considers different features of users to represent their connective and social relationships. Experiments shows that the proposed model outperforms traditional algorithms when applied individually.
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