You're reading from Network Science with Python

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Published inFeb 2023

PublisherPackt

ISBN-139781801073691

Edition1st Edition

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PyTorch Solr

Concepts

Mobile Application Development

Author (1)

David Knickerbocker

Using the Louvain method

The Louvain method is certainly my favorite for community detection, for a few reasons.

First, this algorithm can be used on very large networks of millions of nodes and it will be effective and fast. Other approaches that we will explore in this chapter will not work on large networks and will not be as fast, so we get effectiveness and speed with this algorithm that we can’t find anywhere else. As such, it is my go-to algorithm for community detection, and I save the others as options to consider.

Second, it is possible to tune the resolution parameter to find the best partitions for community detection, giving flexibility when the default results are not optimal. With the other algorithms, you do not have this flexibility.

In summary, with the Louvain method, we have a fast algorithm that is effective at community detection in massive networks, and we can optimize the algorithm for better results. I recommend dabbling in community detection...

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Network Science with Python

Published in: Feb 2023Publisher: PacktISBN-13: 9781801073691

Author (1)

David Knickerbocker

David Knickerbocker is the chief engineer and co-founder of VAST-OSINT. He has over two decades of rich experience working with and around data in his career, with his focus being on data science, data engineering, software development, and cybersecurity.
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Using the Louvain method

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