Mining Social, Mobility Networks: A Random Walk
Visiting Speaker
Past Talk
Hongyang Ryan Zhang
Assistant Professor, Northeastern University
Thursday
Apr 21, 2022
Watch video
Time TBA
11:00 am
177 Huntington Ave
11th floor
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In this talk, I will present several results on estimating and optimizing network centrality: First, I will describe the problem of estimating personalized PageRank and present an estimation method via random walks that enjoy sublinear runtime. Second, I will describe a related problem of estimating shortest distance and show a distance sketch with subquadratic storage space. Third, I will talk about minimizing the eigenvalues of networks with an application to diffusion control. I will close my talk with several questions for future work. This talk is based on joint work with Ashish Goel, Dongyue Li, Huacheng Yu, Peter Lofgren, and Tina Eliassi-Rad.

About the speaker
About the speaker
Hongyang Ryan Zhang is an assistant professor of computer science at Northeastern University since Fall 2020. He received a Ph.D. in computer science from Stanford University in 2019 and a Bachelor of Engineering from Shanghai Jiao Tong University in 2012. He is interested in algorithmic and data-centric aspects of machine learning, deep learning, and networks. His recent and ongoing projects include developing principled methods for the transfer and generalization of machine learning models such as deep neural nets, and the design of efficient algorithms for network centrality and epidemics on social networks and mobility networks.
Hongyang Ryan Zhang is an assistant professor of computer science at Northeastern University since Fall 2020. He received a Ph.D. in computer science from Stanford University in 2019 and a Bachelor of Engineering from Shanghai Jiao Tong University in 2012. He is interested in algorithmic and data-centric aspects of machine learning, deep learning, and networks. His recent and ongoing projects include developing principled methods for the transfer and generalization of machine learning models such as deep neural nets, and the design of efficient algorithms for network centrality and epidemics on social networks and mobility networks.

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