A growing trend of using social networking sites is attracting researchers to study and analyze different aspects of social network. Besides many problems, link prediction is a fascinating problem in the field of social network analysis (SNA). Link prediction, in social network analysis, is a task of identifying the missing links and predicting the new links. Several researchers have proposed solutions for the link prediction problem during the past two decades. However, there is a need to provide comprehensive overview of the significant contributions for a thorough analysis. The objective of this review is to summaries and discuss the existing link prediction algorithms in a common context for an unbiased analysis. The extensive review is presented by constructing the systematical category for proposed algorithms, selected problems, evaluation measures along with selected network datasets. Finally, applications of link prediction are discussed.
Majority of researcher are attracted by the social network analysis due to the rush of people towards social network. Along with many problems, social network analysis is facing an interesting problem that is ranking of users in social network which is gaining more attention due to the increasing number of social users. Measuring centrality of nodes in a social graph, have been important issue in social network analysis. Lot of centrality methods have been proposed in this regard. In this paper, hop based centrality measures called SAM is purposed. To investigate the measure, we applied on various dataset. In comparisons, on all these social graphs, we obtain better results than other centrality measures (i.e., Degree, PageRank, Betweeness and Closeness) using SIR model.
The link prediction has attracted majority of researchers from various domains since the beginning of behavioral science. For instance, online social networks such as Twitter, LinkedIn and Facebook change rapidly as new users appear in the graph. For all these networks, the more challenging task is to find and recommend friends to the users. In case of social graph, the foremost objective of link prediction is to predict which new links are likely to be appearing from the actual state of the graph. Varieties of methods have been developed such as probabilistic, maximum likelihood and similarity-based techniques where similarity-based techniques are considered as the best prediction methods. Similarity-based methods uses a strategy, where each pair of nodes assigned a similarity score such that more similar nodes have more chances to connect in a future. Similarity estimation works on the global and local features i.e. path, random walk and neighbors. Local features are those features of node that consider at node level i.e. adjacent neighbors nodes. On the other hand, global features are those type of features that considers at graph level i.e. path between two nodes. Our hypothesis is that the combination of both local and global features is more powerful predictor for link formation. Here in this study, we have evaluated global, local and hybrid similarity measures. Moreover, we also proposed a hybrid approach GLOS. We performed experiments on five different dataset (Astor, CondMat, GrQc, HepPh and HepTh). After the result evaluation, it is found that, hybrid approach GLOS obtained the highest accuracy by 1 on all the dataset, while, global approaches could not produced lowest accuracy on all dataset. On the other hand, HP from local similarity outperformed than rest of the local and global approaches.
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