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Tuesday, October 26 • 10:30am - 10:45am
Random-Walk Based Graph Representation Learning Revisited

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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)


Arlei Silva

Rice University

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

Attendees (4)