Classification of Trust in Social Networks using Machine Learning Algorithms

Many real-world complex phenomena have dynamic network architectures with nodes and linkages that are added and withdrawn over time. The definition and explanation of network dynamics is a major scientific task, with the classification of short and long-term changes being a vital test. Users connect with one another in these networks, discuss their interests in resources, express their thoughts on those resources, and spread their information . Because each user only has a limited understanding of other users and the majority of them are anonymous, the trust factor is critical in identifying a suitable product or specific individual. In this paper, Advogato and epinion datasets are taken, the various features are calculated for each pair of nodes, and the trust value is prepared. The trust values are classified using the Machine Learning Techniques: Support Vector Machine(SVM), K-Nearest Neighbors(KNN), Logistic Regression, Random Forest, and Light GBM