Channel: PyData

Category: Science & Technology

Tags: pythonlearn to codeeducationsoftwarepydatalearncodinghow to programjuliaopensourcescientific programmingnumfocuspython 3tutorial

Description: Classifying Documents on a Graph using GNNs Speaker: Avi Aminov Summary The usage of graphs to model and solve ML problems is becoming very popular. In this talk we'll review example implementations of Graph ML and how they assist by generating models that take advantage of the individual data point features combined with the graph structure. As an elaborate example, I will present how we use Graph ML to classify document sensitivity without looking at the content. Description Many of the problems we face as scientists can be natively modelled on graph structures that captures both the attributes of objects (nodes) as well as the relationships between them (edges). Such structures include social networks, computer networks, code execution flows, molecular structures, and many more. We will skim briefly through examples (from the last decade) of how Graph ML has been applied successfully to problems including node classification, edge (link) prediction, and whole graph predictions (e.g. in classifying molecules). We will then demonstrate how we at Authomize use Graph Neural Networks to classify different types of sensitive documents by looking at the document’s metadata. The metadata includes the title as well as relationships of the document (node) to other entities (such as employees, or its place in the hierarchy). GNNs allow us to classify these documents without inspecting the content. If time allows, we will dive into how models are delivered in production. Avi Aminov's Bio Avi leads the Data Science efforts at Authomize. He has over a decade of experience in engineering and algorithmic research in various startups and corporates, mainly in e-commerce and cybersecurity. Avi holds a masters in Physics, and once people can go back to staying less than 6ft apart, he would like to go back to dancing and teaching Salsa. GitHub: github.com/bachsh Twitter: twitter.com/AviBachsh LinkedIn: linkedin.com/in/aviaminov PyData Global 2021 Website: pydata.org/global2021 LinkedIn: linkedin.com/company/pydata-global Twitter: twitter.com/PyData pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: github.com/numfocus/YouTubeVideoTimestamps