You are previewing Machine Learning for Hackers.

Machine Learning for Hackers

Cover of Machine Learning for Hackers by Drew Conway... Published by O'Reilly Media, Inc.
  1. Machine Learning for Hackers
  2. Preface
    1. Machine Learning for Hackers
    2. How This Book Is Organized
    3. Conventions Used in This Book
    4. Using Code Examples
    5. Safari® Books Online
    6. How to Contact Us
    7. Acknowledgements
  3. 1. Using R
    1. R for Machine Learning
      1. Downloading and Installing R
      2. IDEs and Text Editors
      3. Loading and Installing R Packages
      4. R Basics for Machine Learning
      5. Further Reading on R
  4. 2. Data Exploration
    1. Exploration versus Confirmation
    2. What Is Data?
    3. Inferring the Types of Columns in Your Data
    4. Inferring Meaning
    5. Numeric Summaries
    6. Means, Medians, and Modes
    7. Quantiles
    8. Standard Deviations and Variances
    9. Exploratory Data Visualization
    10. Visualizing the Relationships Between Columns
  5. 3. Classification: Spam Filtering
    1. This or That: Binary Classification
    2. Moving Gently into Conditional Probability
    3. Writing Our First Bayesian Spam Classifier
      1. Defining the Classifier and Testing It with Hard Ham
      2. Testing the Classifier Against All Email Types
      3. Improving the Results
  6. 4. Ranking: Priority Inbox
    1. How Do You Sort Something When You Don’t Know the Order?
    2. Ordering Email Messages by Priority
      1. Priority Features of Email
    3. Writing a Priority Inbox
      1. Functions for Extracting the Feature Set
      2. Creating a Weighting Scheme for Ranking
      3. Weighting from Email Thread Activity
      4. Training and Testing the Ranker
  7. 5. Regression: Predicting Page Views
    1. Introducing Regression
      1. The Baseline Model
      2. Regression Using Dummy Variables
      3. Linear Regression in a Nutshell
    2. Predicting Web Traffic
    3. Defining Correlation
  8. 6. Regularization: Text Regression
    1. Nonlinear Relationships Between Columns: Beyond Straight Lines
      1. Introducing Polynomial Regression
    2. Methods for Preventing Overfitting
      1. Preventing Overfitting with Regularization
    3. Text Regression
      1. Logistic Regression to the Rescue
  9. 7. Optimization: Breaking Codes
    1. Introduction to Optimization
    2. Ridge Regression
    3. Code Breaking as Optimization
  10. 8. PCA: Building a Market Index
    1. Unsupervised Learning
  11. 9. MDS: Visually Exploring US Senator Similarity
    1. Clustering Based on Similarity
      1. A Brief Introduction to Distance Metrics and Multidirectional Scaling
    2. How Do US Senators Cluster?
      1. Analyzing US Senator Roll Call Data (101st–111th Congresses)
  12. 10. kNN: Recommendation Systems
    1. The k-Nearest Neighbors Algorithm
    2. R Package Installation Data
  13. 11. Analyzing Social Graphs
    1. Social Network Analysis
      1. Thinking Graphically
    2. Hacking Twitter Social Graph Data
      1. Working with the Google SocialGraph API
    3. Analyzing Twitter Networks
      1. Local Community Structure
      2. Visualizing the Clustered Twitter Network with Gephi
      3. Building Your Own “Who to Follow” Engine
  14. 12. Model Comparison
    1. SVMs: The Support Vector Machine
    2. Comparing Algorithms
  15. Works Cited
    1. Books
    2. Articles
  16. Index
  17. About the Authors
  18. Colophon
  19. Copyright

Chapter 11. Analyzing Social Graphs

Social Network Analysis

Social networks are everywhere. According to Wikipedia, there are over 200 active social networking websites on the Internet, excluding dating sites. As you can see from Figure 11-1, according to Google Trends there has been a steady and constant rise in global interest in “social networks” since 2005. This is perfectly reasonable: the desire for social interaction is a fundamental part of human nature, and it should come as no surprise that this innate social nature would manifest in our technologies. But the mapping and modeling of social networks is by no means news.

In the mathematics community, an example of social network analysis at work is the calculation of a person’s Erdős number, which measures her distance from the prolific mathematician Paul Erdős. Erdős was arguably the most prolific mathematician of the 20th century and published over 1,500 papers during his career. Many of these papers were coauthored, and Erdős numbers measure a mathematician’s distance from the circle of coauthors that Erdős enlisted. If a mathematician coauthored with Erdős on a paper, then she would have an Erdős number of one, i.e., her distance to Erdős in the network of 20th-century mathematics is one. If another author collaborated with one of Erdős’ coauthors but not with Erdős directly, then that author would have an Erdős number of two, and so on. This metric has been used, though rarely seriously, as a crude measure of a person’s ...

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