2018/02/20 by Roy Ka-Wei Lee, Lee, Roy Ka-Wei, Tuan-Anh Hoang +4
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Social and Information Networks (cs.SI) #Topic Modeling #cs.IR #cs.SI
paper · pdf · doi:10.48550/arxiv.1802.07022
Pre-print for SIAM International Conference on Data Mining (SDM'18)
arxiv created 2018/02/20 · openalex publication_date 2018/02/20 · arxiv updated 2018/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.