Probabilistic Machine Learning Using Social Network Analysis

The link prediction method analyzes the nodes and edges in the social network. This paper explores the factors to influence the educational opportunities in their institution for the demand of students to choose a valuable educational institution for their higher studies. The original formulation of the nominal nodes has empirically analyzed from different perspectives. The data was collected over multiple time-frequency ranges. We analyzed the classification of nominal nodes using the conditional probability features to calculate the possibilities of an influence. The study discussed the Multinomial Mining Permissible algorithm that is used to classify the learning analytics and decision rules to find the target links to make a community. We found that 91.5% accuracy has able to predict the links in the structural network.