Book Image

Practical Data Science Cookbook, Second Edition - Second Edition

By : Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean Patrick Murphy, ABHIJIT DASGUPTA, Anthony Ojeda
Book Image

Practical Data Science Cookbook, Second Edition - Second Edition

By: Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean Patrick Murphy, ABHIJIT DASGUPTA, Anthony Ojeda

Overview of this book

As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don’t. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface

Finding strong ties


Currently, our hero network simply measures whether two characters are connected. This computation is simple; do they appear in the same comic book together? We assume that in the small temporal space of a comic book, even cameos mean that the characters have interacted with each other. However, this does not tell us who the most important relations for a particular character are.

In order to determine the most important folks in an ego network (or to determine relative affinity between two actors), we need to determine edge weights. Since edges represent interaction, affiliation, or social relations, adding a weight determines the distance between two actors, relative to other actors with similar connections. Proxies for edge weights in social networks include:

  • Frequency, for example, how often two actors communicate
  • Reciprocity, for example, whether or not the relationship is reciprocal
  • Type or attributes, for example, married actors have a stronger tie than college roommates...