Search

One of the major fields of study in the analysis and mining of social networks is the study of social network patterns and development. Link prediction has attracted extensive attention from the scientist in the network science. Despite incredible endeavors to create modern prediction algorithms that can give way better prediction precision in social network topologies that are extricated from complex datasets approximately clients, their exercises, and connections. But most of the recent approaches have not been broadly evaluated. This paper focuses on the missing link prediction between nodes in a systematic way by formalizing and developing approaches about future interactions that can be extracted from network topology for analyzing the proximity measures of nodes, links, and their attributes in a network. The main assumption of this paper is that the missing links must have opposite features concerning the original links. It turns out that feature engineering via measured the node or edge attributes and network structures based provided the best characterization in this sense. Some of network topology-based prediction methods are Common Neighbors, Sørensen Index, Hub Depressed Index, Adamic Index, Salton's cosine formula, spectral clustering, etc. Experimental results using AUC and ROC proved that based on Network topology and previous information may be more or less difficult to accurately predicted future connections so, there is no single clear winner among similarity metrics. [ABSTRACT FROM AUTHOR]