Learning Analysis of Social Networks

December 08, 2021

We, humans, compute new network configurations all the time when thinking about friends and family or colleagues and organizational relations. The established relationships can be personal or professional. A social network consists of a finite set of vertices and the relations defined on them, such network structures always existed, computational social science has helped to reveal and to study them more systematically.

The structure of such networks is usually represented by graphs, networks are often regarded as equivalent to graphs. Two main types of graph-theoretic data structures are referred to represent graphs, list structures and matrix structures. These structures are suitable to store graphs in computers to further analyze them using automatic tools. List structures are suitable for storing sparse graphs since they reduce the required storage space, Matrix structures such as incidence matrices, adjacency matrices or socio matrices and distance matrices are appropriate to represent full matrices. Several types of graphs can be used to model different kinds of social networks.

There are four connection related terms from Social Network Analysis (SNA). Homophily, multiplexity, reciprocity, and propinquity. Homophily refers to the tendency to form ties to other people who share some characteristics such as race, gender, or age, educational status, hobbies, or religious beliefs. Homophily also explores the concept of agency because the agency is the ability of a person to act outside the predicted norm.

Multiplexity refers to the potential for multiple different relationships to exist between two people. Reciprocity implies a relationship that can be represented by one or two edges, where one edge points at both nodes or two edges represent the conjoint nature of the relationship. Propinquity is a type of homophily. People tend to form ties when they are geographically close to one another.

There are many practical applications for social network analysis that can be used to analyze and suggest actions to improve the efficiency of communication within a business organization. SNA can be used to explore how an infectious disease might spread through a group of people, to model traffic patterns, the spread of ideas, how something becomes popular within a given community. Advertisers are interested in understanding how something becomes popular and how individuals influence one another in the adoption of products and services, to turn viral marketing into something that can be understood and can be reproduced is their main goal. When small-world networks emerge, social networking sites thrive. They do whatever they can to encourage their formation such as suggesting you connect with friends or former coworkers.