Social Network Analysis for Startups [2011].pdf

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Table of Contents

Preface

Prerequisites

Open-Source Tools

Conventions Used in This Book

Using Code Examples

Safari® Books Online

How to Contact Us

Thanks

Chapter 1. Introduction

Analyzing Relationships to Understand People and Groups

Binary and Valued Relationships

Symmetric and Asymmetric Relationships

Multimode Relationships

From Relationships to Networks—More Than Meets the Eye

Social Networks vs. Link Analysis

The Power of Informal Networks

Terrorists and Revolutionaries: The Power of Social Networks

Social Networks in Prison

Informal Networks in Terrorist Cells

The Revolution Will Be Tweeted

Social Media and Social Networks

Egyptian Revolution and Twitter

Chapter 2. Graph Theory—A Quick Introduction

What Is a Graph?

Adjacency Matrices

Edge-Lists and Adjacency Lists

7 Bridges of Königsberg

Graph Traversals and Distances

Depth-First Traversal

Implementation

DFS with NetworkX

Breadth-First Traversal

Algorithm

BFS with NetworkX

Paths and Walks

Dijkstra’s Algorithm

Graph Distance

Graph Diameter

Why This Matters

6 Degrees of Separation is a Myth!

Small World Networks

Chapter 3. Centrality, Power, and Bottlenecks

Sample Data: The Russians are Coming!

Get Oriented in Python and NetworkX

Read Nodes and Edges from LiveJournal

Snowball Sampling

Saving and Loading a Sample Dataset from a File

Centrality

Who Is More Important in this Network?

Find the “Celebrities”

Degree centrality in the LiveJournal network

Find the Gossipmongers

Find the Communication Bottlenecks and/or Community Bridges

Putting It Together

Who Is a “Gray Cardinal?”

In practice

Klout Score

PageRank—How Google Measures Centrality

Simplified PageRank algorithm

What Can’t Centrality Metrics Tell Us?

Chapter 4. Cliques, Clusters and Components

Components and Subgraphs

Analyzing Components with Python

Islands in the Net

Subgraphs—Ego Networks

Extracting and Visualizing Ego Networks with Python

Triads

Fraternity Study—Tie Stability and Triads

Triads and Terrorists

The “Forbidden Triad” and Structural Holes

Structural Holes and Boundary Spanning

Triads in Politics

Directed Triads

Analyzing Triads in Real Networks

Real Data

Cliques

Detecting Cliques

Hierarchical Clustering

The Algorithm

Clustering Cities

Preparing Data and Clustering

Block Models

Triads, Network Density, and Conflict

Chapter 5. 2-Mode Networks

Does Campaign Finance Influence Elections?

Theory of 2-Mode Networks

Affiliation Networks

Attribute Networks

A Little Math

2-Mode Networks in Practice

PAC Networks

Candidate Networks

Expanding Multimode Networks

Exercise

Chapter 6. Going Viral! Information Diffusion

Anatomy of a Viral Video

What Did Facebook Do Right?

How Do You Estimate Critical Mass?

Wikinomics of Critical Mass

Content is (Still) King

Heterogenous Preferences

How Does Information Shape Networks (and Vice Versa)?

Birds of a Feather?

Homophily vs. Curiosity

Boundary Spanners

Weak Ties

Dunbar Number and Weak Ties

A Simple Dynamic Model in Python

Influencers in the Midst

Exercises for the Reader

Coevolution of Networks and Information

Exercises for the Reader

Why Model Networks?

Chapter 7. Graph Data in the Real World

Medium Data: The Tradition

Big Data: The Future, Starting Today

“Small Data”—Flat File Representations

EdgeList Files

.net Format

GML, GraphML, and other XML Formats

Ancient Binary Format—##h Files

“Medium Data”: Database Representation

What are Cursors?

What are Transactions?

Names

Nodes as Data, Attributes as ?

The Class

Functions and Decorators

Decorator notation

The Adaptor

Working with 2-Mode Data

Exercises for the Reader

Social Networks and Big Data

NoSQL

Structural Realities

Plain text is king

The freedom to store

Computational Complexities

Big Data is Big

Big Data at Work

What Are We Distributing?

Hadoop, S3, and MapReduce

Hive

SQL is Still Our Friend

Appendix A. Data Collection

A Note on the Ethics of Data Collection

The Old-Fashioned Way

Mining Server Logs

Mining Social Media Sites

Business and Investments

Politics, Elections, and Courts

Blogosphere and Social Bookmarking

Twitter Data Collection

Facebook

Private Ego-Networks

Facebook Social Graph API

Appendix B. Installing Software

Why (We Love) Python?

Exploratory Programming

Python

IPython

NetworkX

matplotlib

pylab: matplotlib with IPython

Social Network Analysis for Startups Maksim Tsvetovat and Alexander Kouznetsov Beijing • Cambridge • Farnham • Köln • Sebastopol • Tokyo

Social Network Analysis for Startups by Maksim Tsvetovat and Alexander Kouznetsov Copyright © 2011 Maksim Tsvetovat and Alexander Kouznetsov. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://my.safaribooksonline.com). For more information, contact our corporate/institutional sales department: (800) 998-9938 or corporate@oreilly.com. Editors: Shawn Wallace and Mike Hendrickson Production Editor: Kristen Borg Proofreader: O’Reilly Production Services Cover Designer: Karen Montgomery Interior Designer: David Futato Illustrator: Robert Romano Revision History for the First Edition: See http://oreilly.com/catalog/errata.csp?isbn=9781449306465 for release details. Nutshell Handbook, the Nutshell Handbook logo, and the O’Reilly logo are registered trademarks of O’Reilly Media, Inc. Social Network Analysis for Startups, the image of a hawfinch, and related trade dress are trademarks of O’Reilly Media, Inc. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and O’Reilly Media, Inc., was aware of a trademark claim, the designations have been printed in caps or initial caps. While every precaution has been taken in the preparation of this book, the publisher and authors assume no responsibility for errors or omissions, or for damages resulting from the use of the information con- tained herein. ISBN: 978-1-449-30646-5 [LSI] 1316789838

Table of Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii 1. Binary and Valued Relationships Symmetric and Asymmetric Relationships Multimode Relationships Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 Analyzing Relationships to Understand People and Groups 2 3 3 3 4 6 10 10 11 14 From Relationships to Networks—More Than Meets the Eye Social Networks vs. Link Analysis The Power of Informal Networks Terrorists and Revolutionaries: The Power of Social Networks Social Networks in Prison Informal Networks in Terrorist Cells The Revolution Will Be Tweeted What Is a Graph? Depth-First Traversal Breadth-First Traversal Paths and Walks Dijkstra’s Algorithm Graph Distance Graph Traversals and Distances Adjacency Matrices Edge-Lists and Adjacency Lists 7 Bridges of Königsberg 2. Graph Theory—A Quick Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 19 21 22 23 25 27 30 31 33 35 36 36 37 37 Graph Diameter Why This Matters 6 Degrees of Separation is a Myth! Small World Networks iii

Centrality Sample Data: The Russians are Coming! Get Oriented in Python and NetworkX Read Nodes and Edges from LiveJournal Snowball Sampling Saving and Loading a Sample Dataset from a File 3. Centrality, Power, and Bottlenecks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 39 39 41 43 44 45 45 45 49 51 54 55 57 58 60 Who Is More Important in this Network? Find the “Celebrities” Find the Gossipmongers Find the Communication Bottlenecks and/or Community Bridges Putting It Together Who Is a “Gray Cardinal?” Klout Score PageRank—How Google Measures Centrality What Can’t Centrality Metrics Tell Us? Subgraphs—Ego Networks Triads Components and Subgraphs Extracting and Visualizing Ego Networks with Python Analyzing Components with Python Islands in the Net 4. Cliques, Clusters and Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 61 62 62 65 65 67 68 68 72 73 74 76 77 79 79 79 81 82 83 84 86 88 Fraternity Study—Tie Stability and Triads Triads and Terrorists The “Forbidden Triad” and Structural Holes Structural Holes and Boundary Spanning Triads in Politics Directed Triads Analyzing Triads in Real Networks Real Data The Algorithm Clustering Cities Preparing Data and Clustering Block Models Triads, Network Density, and Conflict Cliques Detecting Cliques Hierarchical Clustering 5. 2-Mode Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 93 Does Campaign Finance Influence Elections? iv | Table of Contents

Theory of 2-Mode Networks Affiliation Networks Attribute Networks A Little Math 2-Mode Networks in Practice PAC Networks Candidate Networks Expanding Multimode Networks Exercise 96 96 98 98 100 102 102 105 107 Anatomy of a Viral Video How Does Information Shape Networks (and Vice Versa)? What Did Facebook Do Right? How Do You Estimate Critical Mass? Wikinomics of Critical Mass Content is (Still) King 6. Going Viral! Information Diffusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 109 110 111 112 113 116 117 117 119 119 121 125 127 127 133 134 Birds of a Feather? Homophily vs. Curiosity Weak Ties Dunbar Number and Weak Ties A Simple Dynamic Model in Python Influencers in the Midst Exercises for the Reader Coevolution of Networks and Information Exercises for the Reader Why Model Networks? Medium Data: The Tradition Big Data: The Future, Starting Today “Small Data”—Flat File Representations EdgeList Files .net Format GML, GraphML, and other XML Formats Ancient Binary Format—##h Files 7. Graph Data in the Real World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 138 138 139 139 140 141 142 142 143 144 144 145 145 146 What are Cursors? What are Transactions? Names Nodes as Data, Attributes as ? The Class Functions and Decorators “Medium Data”: Database Representation Table of Contents | v

The Adaptor Working with 2-Mode Data Exercises for the Reader Social Networks and Big Data NoSQL Structural Realities Computational Complexities Big Data is Big Big Data at Work What Are We Distributing? Hadoop, S3, and MapReduce Hive SQL is Still Our Friend 148 150 151 151 152 153 156 156 156 157 157 158 160 A. Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 B. Installing Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 vi | Table of Contents

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