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Clojure for Data Science

You're reading from   Clojure for Data Science Statistics, big data, and machine learning for Clojure programmers

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781784397180
Length 608 pages
Edition 1st Edition
Languages
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Author (1):
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Henry Garner Henry Garner
Author Profile Icon Henry Garner
Henry Garner
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Table of Contents (12) Chapters Close

Preface 1. Statistics FREE CHAPTER 2. Inference 3. Correlation 4. Classification 5. Big Data 6. Clustering 7. Recommender Systems 8. Network Analysis 9. Time Series 10. Visualization Index

Whole-graph analysis

Let's turn our attention away from the smaller graphs we've been working with towards the larger graph of followers provided by the twitter_combined.txt file. This contains over 2.4 million edges and will provide a more interesting sample to work with.

One of the simplest metrics to determine about a whole graph is its density. For directed graphs, this is defined as the number of edges |E|, over the number of vertices |V| multiplied by one less than itself.

Whole-graph analysis

For a connected graph (one where every vertex is connected to every other vertex by an edge), the density would be 1. By contrast, a disconnected graph (one with no edges) would have a density of 0. Loom implements graph density as the alg/density function. Let's calculate the density of the larger Twitter graph:

(defn ex-8-17 []
  (->> (load-edges "twitter_combined.txt")
       (apply loom/digraph)
       (alg/density)
       (double)))

;; 2.675E-4

This seems very sparse, but bear in...

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