Analysis of Link Prediction in Directed and Weighted Social Network Structure

The main aim of this paper is to develop algorithms for link prediction based on directed and weighted social network structure. In this paper, the four algorithms such as Modified Common Neighbor (MCN), Modified Jaccard’s Coefficient (MJC), Modified Adamic Adar (MAA) and Modified Preferential Attachment (MPA) has been proposed which is suitable for directed and weighted networks. In our proposed algorithms, the degree of nodes and weightage of each link has been considered. The weightage of each link is assigned using random function. The Modified Common Neighbor (MCN), Modified Jaccard’s Coefficient (MJC), and Modified Adamic Adar (MAA) algorithms are based on an existing Common Neighbor algorithm. The Modified Preferential Attachment (MPA) algorithm depends on the degree of the nodes. The comparative analysis of our proposed algorithms and existing algorithms is performed based on area under receiver operating characteristic values (AUC values), considering different observed links. According to the experimental analysis, it may be concluded that our proposed algorithms provide better performances in comparison to the existing algorithms. Modified Common Neighbor and Modified Adamic Adar results in highest AUC value when twitter dataset and amazon dataset is considered. The proposed algorithms will be applicable in different directed and weighted social network structure for prediction of links between the users.