Book description
Does your startup rely on social network analysis? This concise guide provides a statistical framework to help you identify social processes hidden among the tons of data now available.
Social network analysis (SNA) is a discipline that predates Facebook and Twitter by 30 years. Through expert SNA researchers, you'll learn concepts and techniques for recognizing patterns in social media, political groups, companies, cultural trends, and interpersonal networks. You'll also learn how to use Python and other open source tools—such as NetworkX, NumPy, and Matplotlib—to gather, analyze, and visualize social data. This book is the perfect marriage between social network theory and practice, and a valuable source of insight and ideas.
- Discover how internal social networks affect a company’s ability to perform
- Follow terrorists and revolutionaries through the 1998 Khobar Towers bombing, the 9/11 attacks, and the Egyptian uprising
- Learn how a single special-interest group can control the outcome of a national election
- Examine relationships between companies through investment networks and shared boards of directors
- Delve into the anatomy of cultural fads and trends—offline phenomena often mediated by Twitter and Facebook
Publisher resources
Table of contents
- A Note Regarding Supplemental Files
- Preface
- 1. Introduction
- 2. Graph Theory—A Quick Introduction
-
3. Centrality, Power, and Bottlenecks
- Sample Data: The Russians are Coming!
- Centrality
- What Can’t Centrality Metrics Tell Us?
- 4. Cliques, Clusters and Components
- 5. 2-Mode Networks
- 6. Going Viral! Information Diffusion
- 7. Graph Data in the Real World
- A. Data Collection
- B. Installing Software
- About the Authors
- Copyright
Product information
- Title: Social Network Analysis for Startups
- Author(s):
- Release date: September 2011
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781449306465
You might also like
book
Operations Management: Sustainability and Supply Chain Management, Twelfth Edition
For courses in Operations Management. A broad, practical introduction to operations, reinforced with an extensive collection …
video
Causal inference 101: Answering the crucial "why" in your analysis
Causal questions are ubiquitous in data science. For example, you may have questions that are deeply …
audiobook
The Year in Tech, 2024
A year of HBR's essential thinking on tech-all in one place. Generative AI, Web3, neurotech, reusable …
book
The Rise of the Knowledge Graph
Businesses manage data to understand the connections between their customers, products or services, features, markets, and …