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A supervised learning approach to link prediction in Twitter


The growth of social networks has lately attracted both academic and industrial researchers to study the ties between people, and how the social networks evolve with time. 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. Two main approaches are used to utilize those features; Score-based Approaches and Machine Learning Approaches. In a previous work, a score-based method was used based on topological, node and social features to calculate similarity between users and determine the likelihood of forming future links. This work has been extended by moving to a Machine Learning Approach which treats the prediction process as a classification problem. The classifier predicts the class of each edge whether it exists or doesn’t exist. Machine Learning Approaches have the benefit of adding all similarity indices needed as the feature set fed to the classifier. While in Score-based Approach when we used multiple features with associated weights, the performance was sensitive to the values of such weights. When machine learning is applied, the learning process is performed by the classifier which is fed by eight similarity indices representing connectivity, community, interaction and trust in social network. When indices are combined, a much higher accuracy than the previous Score-based Approach is obtained and hence enhancing the prediction accuracy. 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. Experiments show that using both classical and ensemble classifiers outperforms baseline algorithms when applied individually.

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Correspondence to Cherry Ahmed.

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Ahmed, C., ElKorany, A. & Bahgat, R. A supervised learning approach to link prediction in Twitter. Soc. Netw. Anal. Min. 6, 24 (2016).

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