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Mastering Machine Learning with Spark 2.x

You're reading from   Mastering Machine Learning with Spark 2.x Harness the potential of machine learning, through spark

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781785283451
Length 340 pages
Edition 1st Edition
Languages
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Authors (3):
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Alex Tellez Alex Tellez
Author Profile Icon Alex Tellez
Alex Tellez
Michal Malohlava Michal Malohlava
Author Profile Icon Michal Malohlava
Michal Malohlava
Max Pumperla Max Pumperla
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Max Pumperla
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Basic graph theory

Before diving into Spark GraphX and its applications, we will first define graphs on a basic level and explain what properties they may come with and what structures are worth studying in our context. Along the way of introducing these properties, we will give more concrete examples of graphs that we consider in everyday life.

Graphs

To formalize the notion of a graph briefly sketched in the introduction, on a purely mathematical level, a graph G = (V, E) can be described as a pair of vertices V and edges E, as follows:

V = {v1, ..., vn}

E = {e1, ..., em}

We call the element vi in V a vertex and ei in E an edge, where each edge connecting two vertices v1 and v2 is, in fact, just a pair of vertices, that...

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