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      *Corresponding author: [email protected]

      2

      Introduction To Python for Social Network Analysis

       Agathiya Raja1*, Gavaskar Kanagaraj1 and Mohammad Gouse Galety2

       ¹ Computer Science, Technical University of Clausthal, Clausthal-Zellerfeld, Germany

       ² Department of Information Technology, Catholic University in Erbil, Erbil, Iraq

       Abstract

      A social network is an architecture that consists of the communication among actors, which holds further information about their details and relationship with one another. They are interconnected in the form of edges (or link) and nodes (or vertices). Every social network has its purposes like education, business, consulting, and so on. Social networking platforms play an ever-increasing vital role in almost every field of daily life, including past predictions to future technologies. The intense use of social networking platforms provides a good understanding overview of the community and social behavior. However, well-known projections and conclusions based on analyzing social networking platforms tend to be inexact. A study or analysis on the social network is helpful in many ways (e.g., to find the criminal). Using network-level analysis, one could isolate an objective component/node in a network. One could identify the core, density. One could compute the shortest path, reciprocity, and even homophily. There are incompatible properties among the networks and the network resemblance or connection between multiple networks. Analyzing and visualizing the network using Python offer good insights about the networks to end-users. A high-level programming language provides significant advantages for the end-users and tender vast library packages for integration. Python is an uncomplicated interpreter language, and it is fast to prototype. The language is proposed with several algorithms, which are used to analyze the complex graph. It is incorporated with many packages and libraries, each possessed to perform the desired methodology. The chapter explains the installation and working environment of Python.

      Keywords: Python, social network analysis, Network-X, graph, nevaal

2.1 Introduction

      A social network is an architecture that consists of the communication among actors, which holds further information about their details and relationship with one another. They are interconnected in the form of edges (or links) and nodes (or vertices). Every social network has its purposes, like education, business, consulting, and so on. Social networking platforms play an ever-increasing vital role in almost every field of daily life, including past predictions to future technologies. The intense use of social networking platforms provides a good understanding overview of the community and social behavior. However, well-known projections and conclusions based on analyzing social networking platforms tend to be inexact.

      A study or analysis on the social network is helpful in many ways (e.g., to find the criminal). Using network-level analysis, one could isolate an objective component/node in a network. One could identify the core, density. One could compute the shortest path, reciprocity, and even homophily. There are incompatible properties among the networks and the network resemblance or connection between multiple networks.

      Analyzing and visualizing the network using Python offers good insights about the networks to end-users. A high-level programming language provides significant advantages for the end-users and tender vast library packages for integration. Python is an uncomplicated interpreter language, and it is fast to prototype. The language is proposed with several algorithms which are used to analyze the complex graph. It is incorporated with many packages and libraries, each possessed to perform the desired methodology. This chapter explains the installation and working environment of Python.

      NetworkX is one of the most efficient software packages and an open-source tool in Python. It is mainly used to analyze the complex graph database by manipulating the larger data sets. The chapter explains more about the importance of using Python with desired examples. The installation setup and working environment have been clearly explained in this chapter for better understanding. Although NetworkX is not ideal for large-scale problems with fast-processing requirements,