Analyze Ego-Centric Nodes in Social Network Using Machine Learning Technique

Abstract

Social network is an emerging area for grouping the similar interest people. The empowerment of human interaction is supported by the various services of the social media technologies. It provides the knowledge, inference among the users and their actions. Social media raises the students to discover key influence to choose their higher education institution in their interesting field. It provides constant incitement, prompt, decision, and guidance. The large volume of data has a bridge between the educational institution and students. The links among the users in the social media are playing a vital role in identifying the closeness among the nodes. The framework is used to enhance the properties of links that involves (1) follower of links, (2) followee of links, and (3) follower to followee of links to develop the influence in order to support the career guidance process. The social network metrics are used to evaluate the influence propagation through identifies the influence nodes. The nodes are identified based on the behavioral traits or structural properties of the nodes in the network. The development of career guidance in social network analysis causes an emerging demand for analyzing the closeness of links among the users. The closeness of links with various roles of dimensions using machine learning techniques is used to analyze the efficiency of nodes in the social network.