UoE CS Seminar The unreasonable effectiveness of neural graph embedding - Prof Yong-Yeol “YY” Ahn
Автор: Computer Science - University of Exeter
Загружено: 2023-03-27
Просмотров: 160
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Title: The unreasonable effectiveness of neural graph embedding
Abstract: Graph embedding, or graph representation learning, emerged as an effective, unifying framework to perform numerous machine learning tasks with network data. In this talk, I will discuss how and why one of the most basic graph embedding techniques, node2vec (or word2vec with random walks), works so well, in connection with the theory of random walks and community detection in network science.
Bio: Yong-Yeol (YY) Ahn is an Associate Professor at Indiana University School of Informatics, Computing, and Engineering. He was a Visiting Professor at MIT during 2020-2021. Before joining Indiana University, he worked as a postdoctoral research associate at the Center for Complex Network Research at Northeastern University and as a visiting researcher at the Center for Cancer Systems Biology at Dana-Farber Cancer Institute after earning his PhD in Statistical Physics from KAIST in 2008. His research focuses on developing network science and machine learning methods, and applying them to complex social and biological systems. He is a recipient of several awards including Microsoft Research Faculty Fellowship and LinkedIn Economic Graph Challenge.
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This talk was part of the University of Exeter Computer Science seminar series.
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