Loading…
Back To Schedule
Tuesday, October 26 • 10:30am - 10:45am
Random-Walk Based Graph Representation Learning Revisited

Sign up or log in to save this to your schedule, view media, leave feedback and see who's attending!

Feedback form is now closed.
Technical Presentations Group 1: Algorithms, Foundations, Visualizations, and Engineering Applications

Representation learning is a powerful framework for enabling the application of machine learning to complex data via vector representations. Here, we focus on representation learning for vertices of a graph using random walks. We introduce a framework for node embedding based on three dimensions: type of process, similarity metric, and embedding algorithm. Our framework not only covers many existing approaches but also motivates new ones. In particular, we apply it to produce new state-of-the-art results on link prediction.

Authors: Zexi Huang (UCSB), Arlei Silva (Rice University), and Ambuj Singh (UCSB)

Speakers
AS

Arlei Silva

Rice University


Tuesday October 26, 2021 10:30am - 10:45am CDT

Attendees (4)