Homophily-Based Link Prediction Within a Social Network Using Linguistic Z-number

A crucial component of network analysis in a variety of contexts, including social networks and biological systems, is link prediction. Conventional link prediction techniques frequently depend on deterministic measurements, which are insufficient to reflect the intrinsic uncertainty present in real-world networks. We refer to linguistic Z-Graphs that work well for managing uncertainty. We further improve the ability to make decisions in uncertain contexts by integrating linguistic Z-numbers into the link prediction process. The proposed technique (DSS algorithm) of link prediction is dependent on nodes’ accuracy value, node degree, homophily, and heterophily in order to forecast the likelihood of a link between two vertices in the linguistic Z-Graph network.