NetworkX has established itself as the go-to library for data scientists using Python to study graphs and networks, and for obvious reasons: it's very easy to use, supports a wide variety of graph algorithms, and has a great community supporting it.
But as we amass more and more data, many graph problems reach sizes that exceed the practical capabilities of NetworkX's pure Python implementation, forcing users to abandon NetworkX for another library that's often harder to use.
In response, NetworkX has added the ability to seamlessly dispatch to optimized backends, giving users both ease of use and scalability. This presentation will cover NetworkX for graph analytics and how users can take advantage of dispatching to work with graph data in ways previously not possible.