Kouznetsov - Social Network Analysis for Startups
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Social Network Analysis for Startups: summary, description and annotation
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SNA techniques are derived from sociological and social-psychological theories and take into account the whole network (or, in case of very large networks such as Twitter -- a large segment of the network). Thus, we may arrive at results that may seem counter-intuitive -- e.g. that Jusin Bieber (7.5 mil. followers) and Lady Gaga (7.2 mil. followers) have relatively little actual influence despite their celebrity status -- while a middle-of-the-road blogger with 30K followers is able to generate tweets that go viral and result in millions of impressions. OReillys Mining Social Media and Programming Collective Intelligence books are an excellent start for people inteseted in SNA. This book builds on these books foundations to teach a new, pragmatic, way of doing SNA. I would like to write a book that links theory (why is this important?, how do various concepts interact?, how do I interpret quantitative results?) and practice -- gathering, analyzing and visualizing data using Python and other open-source tools.
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Supplemental files and examples for this book can be found at http://examples.oreilly.com/0636920020424/. Please use a standard desktop web browser to access these files, as they may not be accessible from all ereader devices.
All code files or examples referenced in the book will be available online. For physical books that ship with an accompanying disc, whenever possible, weve posted all CD/DVD content. Note that while we provide as much of the media content as we are able via free download, we are sometimes limited by licensing restrictions. Please direct any questions or concerns to .
Almost every startup company in 2011 uses the word social in their business plansalthough 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 semesters worth of theoretical and practical materialread 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.
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 OReilly book).
We expect you to be at least marginally conversant in Pythoni.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 (OReilly).
We do not cover in detail the process of harvesting data from Twitter, Facebook, and other data sources. Other books in OReillys Animal Guide series provide ample coverage, including Twitter API: Up and Running by Kevin Makice, and Mining the Social Web by Matthew Russell.
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. Gel Varoquaux, Travis Vaught, and Jarrod Millman, pp. 1115, 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
The following typographical conventions are used in this book:
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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 youre 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 OReilly 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 products documentation does require permission.
All code, data, and examples can be downloaded from our GitHub repository at https://github.com/maksim2042/SNABook.
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 (OReilly). Copyright 2011 Maksim Tsvetovat and Alexander Kouznetsov, 978-1-449-30646-5.
If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at .
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