Discovering Hidden Topical Hubs and Authorities in Online Social Networks

TL;DR

This paper introduces the HAT model, a joint topic and link analysis method that identifies influential users in social networks more effectively than existing approaches, especially for link recommendation.

Contribution

The paper proposes the novel HAT model that jointly learns user topics and hub/authority scores, integrating topic modeling with link analysis.

Findings

HAT performs comparably to state-of-the-art topic models in topic learning.

HAT outperforms existing methods in link recommendation tasks.

Experiments on Twitter and Instagram datasets validate HAT's effectiveness.

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 combine 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.