Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2018

Abstract

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.

Keywords

HITS algorithms, Link analysis, On-line social networks, Real-world datasets, State of the art, State-of-the-art methods, Topic model, Topic Modeling

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media

Research Areas

Data Science and Engineering

Publication

Proceedings of the SIAM International Conference on Data Mining (SDM18): San Diego, CA, May 3-5

First Page

378

Last Page

386

Identifier

10.1137/1.9781611975321.43

Publisher

SIAM

City or Country

Philadelphia

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1137/1.9781611975321.43

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