An Overview of Similarity-Based Methods in Predicting Social Network Links: A Comparative Analysis

Link prediction in the Social Network is most important and an essential part now a days. The continued growth and evolution of this field will lead to new and improved methods for analyzing and understanding social networks. Link prediction is also helpful in various network applications in both academic and real-world contexts. For better understanding of prediction of links in a network graph through the use of different algorithms and information of prediction of missing link between network that all of the clear information is discuss in this paper. This paper presents the study of different types of algorithms which are better informative to understand the connection prediction, in a methodical manner. For this study, the similarity approaches are concentrated with its types of algorithms which are used to forecast the presence of missing links in social networks. This paper addresses the various link prediction approaches considering the structure of the network to reduce uncertainty. Evaluation measures for link prediction and their practical applications are also covered in this work. Lastly, it discusses the difficulties and provides plans for the development of link prediction methods in the future. This discussion may help researchers to choose the proper network structure for predicting the links.