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Discovering Hidden Topical Hubs and Authorities in Online Social Networks
Speaker (s):
Roy LEE Ka Wei
PhD Candidate
School of Information Systems
Singapore Management University
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Date:
Time:
Venue:
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February 23, 2018, Friday
10:30am - 11:00am
Meeting Room 5.1, Level 5
School of Information Systems
Singapore Management University
80 Stamford Road
Singapore 178902
We look forward to seeing you at this research seminar.
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ABOUT THE TALK
Finding influential users in online social networks is an important problem with many possible useful applications. HITS and other link analysis methods, in particular, have been often used to identify hub and authority users in web graphs and online social networks. These works, however, have not considered topical aspect of links in their analysis. A straightforward approach to overcome this limitation is to first apply topic models to learn the user topics before applying the HITS algorithm. In this paper, we instead propose a novel topic model known as Hub and Authority Topic (HAT) model to combines the two process so as to jointly learn the hub, authority and topical interests. We evaluate HAT against several existing state-of-the-art methods in two aspects: (i) modeling of topics, and (ii) link recommendation. We conduct experiments on two real-world datasets from Twitter and Instagram. Our experiment results show that HAT is comparable to state-of-the-art topic models in learning topics and it outperforms the state- of-the-art in link recommendation task.
This a pre-conference talk for SIAM International Conference on Data Mining (SDM18).
About the Speaker
Roy Ka-Wei Lee is a PhD candidate in School of Information Systems, Singapore Management University, working with Professor Lim Ee-Peng. He received his Bachelor of Science (IS Management) and Master of Applied Information System from Singapore Management University. His main research interests are social network analysis and data mining. His current research focuses on studying the user behaviors and information diffusion across multiple social networks.
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