Practical Geometric Deep Learning in Python
YOW! Data 2019
Geometric Deep Learning (GDL) is a fast developing machine learning specialisation that uses the network structure underlying the data to improve learning outcomes. GDL has been successfully applied to problems in various domains with network-structured data, such as social science, medicine, media, finance, etc.
Inspired by the success of neural networks in domains such as computer vision and natural language processing, the core component driving GDL is the graph convolution operator. This operator is used as the building block for deep learning models applied to networks. This approach takes advantage of many algorithmic and computational developments from modern neural network research and practice – such as composability, optimisation, and end-to-end training – to improve predictive performance.
However, there is a lack of tools for geometric deep learning targeting data scientists and machine learning practitioners.
In response, CSIRO’s Data61 has developed StellarGraph, an open source Python library. StellarGraph implements a number of state-of-the-art methods for GDL with a clean and consistent API. Furthermore, StellarGraph is designed to make the application of GDL algorithms to network-structured data easy to integrate with existing machine learning workflows.
In this talk, we will start with an overview of GDL and its real-world applications. Then we will introduce StellarGraph with a focus on its design philosophy, API and analytics workflow. Finally, we will demonstrate StellarGraph’s flexibility and ease-of-use for developing solutions targeting important applications such as product recommendation and social network moderation. Lastly, we will touch on the challenges of designing and implementing a library for a fast evolving machine learning field.