The Python Podcast.__init__

Summary

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.

Announcements

• Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.


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• 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

Interview

• Introductions


• How did you get introduced to Python?


• Can you describe what graph-tool is and the story behind it?


• What are some scenarious where someone might encounter a graph oriented data set?
  
  
  
  • In what ways are those graphs typically represented?
  
  
  • 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.)
  
  
  
  


• What kinds of analysis or manipulation might someone need to perform on a graph structure?


• 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?


• Can you describe how graph-tool is implemented?
  
  
  
  • How have the goals and design of the package changed or evolved since you first began working on it?
  
  
  
  


• Who are your target users and what are the guiding principles that you use to inform the API design for the package?
  
  
  
  • How much knowledge of graph theory or algorithms are required to make effective use of graph-tool?
  
  
  
  


• Can you talk through an example workflow of using graph-tool to load, process, and analyze a graph?


• What are some of the overlooked or underutilized aspects of graph-tool that you think more people should know about?


• What are some systems/applications that you have seen which would be simplified by adopting a graph model for their data?
  
  
  
  • What is your impression of the overall awareness of the benefits of graphs for simplifying aspects of data processing and analysis?
  
  
  
  


• What are some cases where a graph structure adds unnecessary complexity?


• What are the most interesting, innovative, or unexpected ways that you have seen graph-tool used?


• What are the most interesting, unexpected, or challenging lessons that you have learned while working on graph-tool?


• When is graph-tool the wrong choice?


• What do you have planned for the future of graph-tool?

Keep In Touch

• Website


• graph-tool

Picks

• Tobias
  
  
  
  • 97 Things Every Data Engineer Should Know
  
  
  
  

Closing Announcements

• Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.


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Links

• Central European University


• NetworkX


• GML


• GraphML


• Neo4J


• DGraph
  
  
  
  • Data Engineering Podcast Episode
  
  
  
  


• NetworKit


• igraph


• Matplotlib


• C++ Templates


• Boost Graph Library


• OpenMP


• Maximum Matching

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA