Social Network Analysis for Startups

Preface

Almost every startup company in 2011 uses the word “social” in their business plans—although few actually know how to analyze and understand the social processes that can result in their firms’ success or failure. If you are working in social media, social CRM, social marketing, organizational consulting, etc., you should read this book for insights on how social systems evolve and change, and how to detect what is going on.

Despite the title, the book is not just for startups. In fact, it is a “course-in-a-book”, encapsulating nearly a semester’s worth of theoretical and practical material—read it and you will know enough about social network analysis to be “dangerous”. If you are a student in the field, we strongly encourage you to seek out and read every paper and book referred to in the footnotes. This will make you conversant in the classic literature of the field and enable you to confidently start your own research project.

If you are of a technical or computer science background, this book will introduce you to major sociological concepts and tie them back into things that can be programmed, and data that can be analyzed. If your background is social sciences or marketing, you may find some of the background material familiar, but at the same time will learn the quantitative and programmatic approaches to understanding humans in a social setting.

Prerequisites

This books is written to be accessible by a wide audience. We keep jargon to a minimum, and explain terms as they come along. However, there is a serious amount of technical content (as one expects from an O’Reilly book).

We expect you to be at least marginally conversant in Python—i.e., able to write your own scripts, understand the basic control structures and data structures of the language. If you are not yet there, we suggest starting with an online Python tutorial or Head First Python by Paul Barry (O’Reilly).

We do not cover in detail the process of harvesting data from Twitter, Facebook, and other data sources. Other books in O’Reilly’s “Animal Guide” series provide ample coverage, including Twitter API: Up and Running by Kevin Makice, and Mining the Social Web by Matthew Russell.

Open-Source Tools

This book utilizes open-source Python libraries including NetworkX, NumPy, and MatPlotLib.

• NetworkX packages and documentation can be found at http://networkx.lanl.gov/.

  Aric A. Hagberg, Daniel A. Schult and Pieter J. Swart, “Exploring network structure, dynamics, and function using NetworkX”, in Proceedings of the 7th Python in Science Conference (SciPy2008), eds. Gäel Varoquaux, Travis Vaught, and Jarrod Millman, pp. 11–15, Aug 2008.

• NumPy packages and documentation are at http://numpy.scipy.org/.

  Oliphant, Travis E. “Python for Scientific Computing”. Computing in Science & Engineering 9(2007), 10-20 Ascher, D. et al. Numerical Python, tech. report UCRL-MA-128569, Lawrence Livermore National Laboratory, 2001.

• MatplotLib can be found at http://matplotlib.sourceforge.net.

  Hunter, J.D. “Matplotlib: A 2D Graphics Environment”. Computing in Science & Engineering 9(2007), 90-95

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Using Code Examples

This book is here to help you get your job done. In general, you may use the code in this book in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission.

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We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Social Network Analysis for Startups by Maksim Tsvetovat and Alexander Kouznetsov (O’Reilly). Copyright 2011 Maksim Tsvetovat and Alexander Kouznetsov, 978-1-449-30646-5.”

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Content Updates

March 16, 2012

Fixed typographical errors.

Thanks

Max Tsvetovat would like to thank his teachers, Sergei Fyodorovich Ivanov (High School #367, St. Petersburg, Russia), Maria Gini (University of Minnesota), and Kathleen Carley and Katya Sycara (Carnegie Mellon University), for putting up with an unruly student for so many years—and most importantly, for teaching me to think on my feet and to learn on my own. He thanks his family for their encouragement and patience during late night writing sessions (and specifically little David for sleeping through the night and letting Daddy work). Music by Miles Davis. Coffee by NorthSide Social. Wardrobe by…darn it, who am I kidding?

Alex Kouznetsov owes thanks to many friends, teachers, and colleagues who each did their part in shaping his path. He thanks his family for the support, and especially his wife Natalia for her active interest and taking care of life’s logistics. Special thanks go to Nikolai Afanasievich Kolobov (Moscow Institute of Electronics and Mathematics) for demonstrating the joy of unorthodox thinking, scientific rigor, and a subsequent switch to natural sciences; and a longtime friend Javier Sanchez, for teaching me how to think critically and learn on my own, for fun and profit.