Social Network Analysis for Startups by Tsvetovat, Maksim -- Read -- Imperial Library of Trantor

Index

Social Network Analysis for Startups SPECIAL OFFER: Upgrade this ebook with O’Reilly A Note Regarding Supplemental Files Preface

Prerequisites Open-Source Tools Conventions Used in This Book Using Code Examples Safari® Books Online How to Contact Us Content Updates

March 16, 2012

Thanks

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

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

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?

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

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

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?

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

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

B. Installing Software

Why (We Love) Python? Exploratory Programming Python IPython NetworkX matplotlib

pylab: matplotlib with IPython

About the Authors SPECIAL OFFER: Upgrade this ebook with O’Reilly Copyright

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