An Approach for Exploring Practical Phenomena In Social Network Analysis

Abstract

Social networks like Facebook, LinkedIn, Twitter, WhatsApp, etc. have appeared more often these days, and their importance and influence in human life are growing rapidly. A social network is useful for connecting people to communicate and share information with one another in a virtual setting. A member of a social network can establish relationships with individual members or groups of members in that network. Utilizing networks and graph theory to construct social structures is the process known as social network analysis (SNA). The mapping and measurement of relationships and flows between individuals, groups, organizations, and so on is known as social network analysis (SNA). Social networks are represented using the concept of graph theory, where members of a social network are represented as nodes, and relationships between members are represented by edges, representing links, connections, and so on. The nodes represent the members of the social network, and the relationships between them are formed by the concept that there is either a direct or indirect path between them. Generally, members of social networks can establish relationships with each other using a one-to-one or one-to-many concept. The manner in which two or more members of a social network communicate or act toward one another constitutes relationships. The representation of social networks using graph theory helps to understand the trends in research on the relationships between different types of nodes and to predict the behaviour of nodes in social network analysis. The natural process of learning by doing is the subject of a subfield of computer science called machine learning (ML). ML assists in identifying patterns and the structure of node-to-node relationships in social networks. This paper attempts to study relationships among members of social networks using mathematical graph theory and machine learning.