Book Image

Mastering Spark for Data Science

By : Bifet, Morgan, Amend, Hallett, George
Book Image

Mastering Spark for Data Science

By: Bifet, Morgan, Amend, Hallett, George

Overview of this book

Data science seeks to transform the world using data, and this is typically achieved through disrupting and changing real processes in real industries. In order to operate at this level you need to build data science solutions of substance –solutions that solve real problems. Spark has emerged as the big data platform of choice for data scientists due to its speed, scalability, and easy-to-use APIs. This book deep dives into using Spark to deliver production-grade data science solutions. This process is demonstrated by exploring the construction of a sophisticated global news analysis service that uses Spark to generate continuous geopolitical and current affairs insights.You will learn all about the core Spark APIs and take a comprehensive tour of advanced libraries, including Spark SQL, Spark Streaming, MLlib, and more. You will be introduced to advanced techniques and methods that will help you to construct commercial-grade data products. Focusing on a sequence of tutorials that deliver a working news intelligence service, you will learn about advanced Spark architectures, how to work with geographic data in Spark, and how to tune Spark algorithms so they scale linearly.
Table of Contents (15 chapters)

Community detection algorithm


Community detection has become a popular field of research over the past few decades. Sadly, it did not move as fast as the digital world that a true data scientist lives in, with more and more data collected every second. As a result, most of the proposed solutions are simply not suitable for a big data environment.

Although a lot of algorithms suggest a new scalable way for detecting communities, none of them is actually meaning scalable in a sense of distributed algorithms and parallel computing.

Louvain algorithm

Louvain algorithm is probably the most popular and widely used algorithm for detecting communities on undirected weighted graphs.

Note

For more information about Louvain algorithm, refer to the publication: Fast unfolding of communities in large networks. Vincent D. Blondel, Jean-Loup Guillaume, Renaud Lambiotte, Etienne Lefebvre. 2008

The idea is to start with each vertex being the center of its own community. At each step, we look for community neighbors...