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Graph algorithms: comparison with current algorithms textbooks

 
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Hi, Mark and Amy,

Thank you for publishing this new book. I have read several classical algorithms textbooks, including Algorithms (4th Edition by Robert Sedgewick and Kevin Wayne, Princeton University Press) and Algorithms (by Jeff Erickson). These textbooks have spent many chapters on graph algorithms.

Can you quickly describe the relationship (e.g. similarities and differences) between your new book and those algorithms textbooks?

Thank you. I am looking forward to reading your new book!

Best,

Sean
 
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Hi Sean,

I love book recommendations! I've added yours to my queue.  

After looking at the chapters, it seems the two that mentioned cover some graph algorithms with particuliar focus on the pathfinding and how the algorithms work.  

This Graph Algorithms books includes pathfinding, community detection, and centarlity algorithms. (We also slipped some Link Prediction algos into the last chapter.) We spend a lot of time on helping people understand when to use the different algorithms with examples. And then we have 2 chapters using fictional workflow to provide an end-to-end feel for how they might work in a real-work scenario.  So overall, I'd say this book is intended as a practical guide to give people the confidence to apply graph algorithms in either Spark or Neo4j. There's some overlap in that we also try to explain how these algorithms work but we're lighter on theory and heavier on usage.

Now I have to share a list of books I like.  
https://neo4j.com/blog/top-13-resources-graph-theory-algorithms/
My all-time algo favorite is The Algorithms Design Manual by Dr Skiena.
 
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