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.
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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
- 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?
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