Date: Tue, 06 Jul 2021 22:00:00 -0400
<div class="wp-block-jetpack-markdown"><h2>Summary</h2> <p>If you are interested in a library for working with graph structures that will also help you learn more about the research and theory behind the algorithms then look no further than graph-tool. In this episode Tiago Peixoto shares his work on graph algorithms and networked data and how he has built graph-tool to help in that research. He explains how it is implemented, how it evolved from a simple command line tool to a full-fledged library, and the benefits that he has found from building a personal project in the open.</p> <h2>Announcements</h2> <ul> <li>Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.</li> <li>When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. 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And don’t forget to thank them for their continued support of this show!</li> <li>Your host as usual is Tobias Macey and today I’m interviewing Tiago Peixoto about graph-tool, an efficient Python module for manipulation and statistical analysis of graphs</li> </ul> <h2>Interview</h2> <ul> <li>Introductions</li> <li>How did you get introduced to Python?</li> <li>Can you describe what graph-tool is and the story behind it?</li> <li>What are some scenarious where someone might encounter a graph oriented data set? <ul> <li>In what ways are those graphs typically represented?</li> <li>In your experience, what is the overlap of people who are working with networked data, and the use of graph-native databases? (e.g. Neo4J, DGraph, etc.)</li> </ul> </li> <li>What kinds of analysis or manipulation might someone need to perform on a graph structure?</li> <li>There are a few different tools in Python for working with networked data. How would you characterize the current ecosystem and why someone might choose graph-tool?</li> <li>Can you describe how graph-tool is implemented? <ul> <li>How have the goals and design of the package changed or evolved since you first began working on it?</li> </ul> </li> <li>Who are your target users and what are the guiding principles that you use to inform the API design for the package? <ul> <li>How much knowledge of graph theory or algorithms are required to make effective use of graph-tool?</li> </ul> </li> <li>Can you talk through an example workflow of using graph-tool to load, process, and analyze a graph?</li> <li>What are some of the overlooked or underutilized aspects of graph-tool that you think more people should know about?</li> <li>What are some systems/applications that you have seen which would be simplified by adopting a graph model for their data? <ul> <li>What is your impression of the overall awareness of the benefits of graphs for simplifying aspects of data processing and analysis?</li> </ul> </li> <li>What are some cases where a graph structure adds unnecessary complexity?</li> <li>What are the most interesting, innovative, or unexpected ways that you have seen graph-tool used?</li> <li>What are the most interesting, unexpected, or challenging lessons that you have learned while working on graph-tool?</li> <li>When is graph-tool the wrong choice?</li> <li>What do you have planned for the future of graph-tool?</li> </ul> <h2>Keep In Touch</h2> <ul> <li><a href="https://skewed.de/tiago?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">Website</a></li> <li><a href="https://graph-tool.skewed.de?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">graph-tool</a></li> </ul> <h2>Picks</h2> <ul> <li>Tobias <ul> <li><a href="https://amzn.to/3gOzQit?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">97 Things Every Data Engineer Should Know</a></li> </ul> </li> </ul> <h2>Closing Announcements</h2> <ul> <li>Thank you for listening! Don’t forget to check out our other show, the <a href="https://feeds.fireside.fm/pythonpodcast/rss">Data Engineering Podcast</a> for the latest on modern data management.</li> <li>Visit the <a href="https://www.pythonpodcast.com?utm_source=rss&utm_medium=rss">site</a> to subscribe to the show, sign up for the mailing list, and read the show notes.</li> <li>If you’ve learned something or tried out a project from the show then tell us about it! Email <a href="mailto:hosts@podcastinit.com">hosts@podcastinit.com</a>) with your story.</li> <li>To help other people find the show please leave a review on <a href="https://itunes.apple.com/us/podcast/podcast.-init/id981834425?mt=2&uo=6&at=&ct=&utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">iTunes</a> and tell your friends and co-workers</li> <li>Join the community in the new Zulip chat workspace at <a href="https://www.pythonpodcast.com/chat?utm_source=rss&utm_medium=rss">pythonpodcast.com/chat</a></li> </ul> <h2>Links</h2> <ul> <li><a href="https://www.ceu.edu/?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">Central European University</a></li> <li><a href="https://feeds.fireside.fm/pythonpodcast/networkx.github.io/">NetworkX</a></li> <li><a href="https://en.wikipedia.org/wiki/Graph_Modelling_Language?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">GML</a></li> <li><a href="https://en.wikipedia.org/wiki/GraphML?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">GraphML</a></li> <li><a href="https://neo4j.com/?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">Neo4J</a></li> <li><a href="https://dgraph.io/?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">DGraph</a> <ul> <li><a href="https://www.dataengineeringpodcast.com/dgraph-with-manish-jain-episode-44/?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">Data Engineering Podcast Episode</a></li> </ul> </li> <li><a href="https://networkit.github.io/?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">NetworKit</a></li> <li><a href="https://igraph.org/redirect.html?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">igraph</a></li> <li><a href="https://matplotlib.org/?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">Matplotlib</a></li> <li><a href="https://en.cppreference.com/w/cpp/language/templates?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">C++ Templates</a></li> <li><a href="https://www.boost.org/doc/libs/1_76_0/libs/graph/doc/index.html?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">Boost Graph Library</a></li> <li><a href="https://www.openmp.org/?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">OpenMP</a></li> <li><a href="https://en.wikipedia.org/wiki/Matching_(graph_theory)#Maximal_matchings?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">Maximum Matching</a></li> </ul> <p>The intro and outro music is from Requiem for a Fish <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">The Freak Fandango Orchestra</a> / <a href="http://creativecommons.org/licenses/by-sa/3.0/?utm_source=rss&utm_medium=rss" rel="noopener" target="_blank">CC BY-SA</a></p> </div> <img alt="" height="0" src="https://analytics.boundlessnotions.com/piwik.php?idsite=1&rec=1&url=https%3A%2F%2Fwww.pythonpodcast.com%2Fgraph-tool-graph-data-analysis-episode-322%2F&action_name=Fast+And+Educational+Exploration+And+Analysis+Of+Graph+Data+Structures+With+graph-tool+-+Episode+322&urlref=https%3A%2F%2Fwww.pythonpodcast.com%2Ffeed%2F&utm_source=rss&utm_medium=rss" style="border: 0; width: 0; height: 0;" width="0" />