Fast and Accurate Random Walk with Restart

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Fast and Accurate Random Walk with Restart



U Kang is an associate professor in the Department of Computer Science and Engineering of Seoul National University.

He received Ph.D. in Computer Science at Carnegie Mellon University,

after receiving B.S. in Computer Science and Engineering at Seoul National University.

He won 2013 SIGKDD Doctoral Dissertation Award, 2013 New Faculty Award from Microsoft Research Asia,

2016 Korean Young Information Scientist Award,

and two best paper awards.

He has published over 60 refereed articles in major data mining and database venues.

He holds four U.S. patents.

His research interests include big data mining and machine learning.




Given a large graph like a phone user network, and an IoT connection graph, how can we calculate the relevance between nodes fast and accurately?

Random walk with restart (RWR) provides an excellent measure for this purpose and has been applied to diverse data mining applications including ranking, community detection, link prediction, and anomaly detection.


In this talk, I will introduce the state-of-the-art algorithms for RWR, and describe various applications RWR in real world.

I will also share my experiences on how to invent new knowledges through research.